共查询到20条相似文献,搜索用时 31 毫秒
1.
Inductive logic programming (ILP) is concerned with the induction of logic programs from examples and background knowledge.
In ILP, the shift of attention from program synthesis to knowledge discovery resulted in advanced techniques that are practically
applicable for discovering knowledge in relational databases. This paper gives a brief introduction to ILP, presents selected
ILP techniques for relational knowledge discovery and reviews selected ILP applications.
Nada Lavrač, Ph.D.: She is a senior research associate at the Department of Intelligent Systems, J. Stefan Institute, Ljubljana, Slovenia (since
1978) and a visiting professor at the Klagenfurt University, Austria (since 1987). Her main research interest is in machine
learning, in particular inductive logic programming and intelligent data analysis in medicine. She received a BSc in Technical
Mathematics and MSc in Computer Science from Ljubljana University, and a PhD in Technical Sciences from Maribor University,
Slovenia. She is coauthor of KARDIO: A Study in Deep and Qualitative Knowledge for Expert Systems, The MIT Press 1989, and
Inductive Logic Programming: Techniques and Applications, Ellis Horwood 1994, and coeditor of Intelligent Data Analysis in
Medicine and Pharmacology, Kluwer 1997. She was the coordinator of the European Scientific Network in Inductive Logic Programming
ILPNET (1993–1996) and program cochair of the 8th European Machine Learning Conference ECML’95, and 7th International Workshop
on Inductive Logic Programming ILP’97.
Sašo Džeroski, Ph.D.: He is a research associate at the Department of Intelligent Systems, J. Stefan Institute, Ljubljana, Slovenia (since 1989).
He has held visiting researcher positions at the Turing Institute, Glasgow (UK), Katholieke Universiteit Leuven (Belgium),
German National Research Center for Computer Science (GMD), Sankt Augustin (Germany) and the Foundation for Research and Technology-Hellas
(FORTH), Heraklion (Greece). His research interest is in machine learning and knowledge discovery in databases, in particular
inductive logic programming and its applications and knowledge discovery in environmental databases. He is co-author of Inductive
Logic Programming: Techniques and Applications, Ellis Horwood 1994. He is the scientific coordinator of ILPnet2, The Network
of Excellence in Inductive Logic Programming. He was program co-chair of the 7th International Workshop on Inductive Logic
Programming ILP’97 and will be program co-chair of the 16th International Conference on Machine Learning ICML’99.
Masayuki Numao, Ph.D.: He is an associate professor at the Department of Computer Science, Tokyo Institute of Technology. He received a bachelor
of engineering in electrical and electronics engineering in 1982 and his Ph.D. in computer science in 1987 from Tokyo Institute
of Technology. He was a visiting scholar at CSLI, Stanford University from 1989 to 1990. His research interests include Artificial
Intelligence, Global Intelligence and Machine Learning. Numao is a member of Information Processing Society of Japan, Japanese
Society for Artificial Intelligence, Japanese Cognitive Science Society, Japan Society for Software Science and Technology
and AAAI. 相似文献
2.
James V. Hansen James B. McDonald Conan C. Albrecht Douglas L. Dean Bonnie B. Anderson 《Electronic Commerce Research》2007,7(1):5-16
This paper proposes an adaptive learning approach that yields decision models that can be applied by a transactions agent.
This model can learn effectively with a variety of data distributions. This research uses the Semantic Web as a data access
approach. The Semantic Web is a method that sellers can use to publish semantically meaningful information on Websites so
automated applications can reliably access that information. We implemented a Semantic Web composed of 30 vendors’ Web pages
and a spider to search those pages to obtain product and vendor information. This information was used to train a learning
agent, which then provided a decision model to a transaction agent.
James Hansen is J. Owen Cherrington Professor in the Information Systems Department of the Marriott School of Management at Brigham Young
University. He is an associate editor for IEEE Intelligent Systems and Information Systems Frontiers. His research is in machine learning and planning as model checking.
James B. McDonald is Professor of Economics at Brigham Young University. His research interests are in econometrics and quantitative methods.
He has recently published in Econometrica, Journal of the American Statistical Association, Management Science, and Journal of Business
Conan C. Albrecht is a professor of Information Systems at Brigham Young University. He teaches classes in enterprise development, middleware,
and business programming.
Conan researches computer-based fraud detection techniques, ecommerce platforms, and online group dynamics. He has published
articles on fraud detection and information theory in The Journal of Forensic Accounting, The Journal of Accounting, The Communications of the ACM, Decision Support Systems, Information and Management, and other academic and professional outlets.
Conan is currently working on an open source framework for computer-based fraud detection. The core of this research is detectlets,
which encode background and detection information for specific fraud schemes. He is researching with the United Nations and
the World Bank to use detectlets to prevent and detect fraud in third world countries. In the next few years, he hopes the
system will serve as the foundation of a large, online repository of detectlets about all types of fraud.
Douglas L. Dean is an Associate Professor at the Marriott School of Management at Brigham Young University. He is also research coordinator
for the Rollins Center for E-business. He received his Ph.D. in MIS from the University of Arizona in 1995. Dr. Dean’s research
interests include electronic commerce technology and strategy, online communities, requirements analysis, and collaborative
tools and methods. His work has been published in Management Science, Journal of Management Information Systems, Information and Management, The DATA BASE for Advances in Information Systems,
Communications of the AIS, Expert Systems with Applications, Group Decision and Negotiation, and IEEE Transactions on Systems,
Man, and Cybernetics.
Bonnie Brinton Anderson is the LeAnn Albrecht Fellow and an Assistant Professor in the Information Systems Department of the Marriott School at Brigham
Young University (Provo, UT). She received her Ph.D. from Carnegie Mellon University. Dr. Anderson has published in Decision Support Systems; IEEE Transactions on Systems, Man, and Cybernetics; Communications of the ACM; Journal of Accountancy, among others. She researches in the areas of knowledge management, information systems security, and intelligent agents. 相似文献
3.
In this paper. we present the MIFS-C variant of the mutual information feature-selection algorithms. We present an algorithm
to find the optimal value of the redundancy parameter, which is a key parameter in the MIFS-type algorithms. Furthermore,
we present an algorithm that speeds up the execution time of all the MIFS variants. Overall, the presented MIFS-C has comparable
classification accuracy (in some cases even better) compared with other MIFS algorithms, while its running time is faster.
We compared this feature selector with other feature selectors, and found that it performs better in most cases. The MIFS-C
performed especially well for the breakeven and F-measure because the algorithm can be tuned to optimise these evaluation measures.
Jan Bakus received the B.A.Sc. and M.A.Sc. degrees in electrical engineering from the University of Waterloo, Waterloo, ON, Canada,
in 1996 and 1998, respectively, and Ph.D. degree in systems design engineering in 2005. He is currently working at Maplesoft,
Waterloo, ON, Canada as an applications engineer, where he is responsible for the development of application specific toolboxes
for the Maple scientific computing software.
His research interests are in the area of feature selection for text classification, text classification, text clustering,
and information retrieval. He is the recipient of the Carl Pollock Fellowship award from the University of Waterloo and the
Datatel Scholars Foundation scholarship from Datatel.
Mohamed S. Kamel holds a Ph.D. in computer science from the University of Toronto, Canada. He is at present Professor and Director of the
Pattern Analysis and Machine Intelligence Laboratory in the Department of Electrical and Computing Engineering, University
of Waterloo, Canada. Professor Kamel holds a Canada Research Chair in Cooperative Intelligent Systems.
Dr. Kamel's research interests are in machine intelligence, neural networks and pattern recognition with applications in robotics
and manufacturing. He has authored and coauthored over 200 papers in journals and conference proceedings, 2 patents and numerous
technical and industrial project reports. Under his supervision, 53 Ph.D. and M.A.Sc. students have completed their degrees.
Dr. Kamel is a member of ACM, AAAI, CIPS and APEO and has been named s Fellow of IEEE (2005). He is the editor-in-chief of
the International Journal of Robotics and Automation, Associate Editor of the IEEE SMC, Part A, the International Journal
of Image and Graphics, Pattern Recognition Letters and is a member of the editorial board of the Intelligent Automation and
Soft Computing. He has served as a consultant to many Companies, including NCR, IBM, Nortel, VRP and CSA. He is a member of
the board of directors and cofounder of Virtek Vision International in Waterloo. 相似文献
4.
C. Urdiales E. J. Perez J. Vázquez-Salceda M. Sànchez-Marrè F. Sandoval 《Autonomous Robots》2006,21(1):65-78
This paper presents a new sonar based purely reactive navigation technique for mobile platforms. The method relies on Case-Based
Reasoning to adapt itself to any robot and environment through learning, both by observation and self experience. Thus, unlike
in other reactive techniques, kinematics or dynamics do not need to be explicitly taken into account. Also, learning from
different sources allows combination of their advantages into a safe and smooth path to the goal. The method has been succesfully
implemented on a Pioneer robot wielding 8 Polaroid sonar sensors.
Cristina Urdiales is a Lecturer at the Department of Tecnología Electrónica (DTE) of the University of Málaga (UMA). She received a MSc degree
in Telecommunication Engineering at the Universidad Politécnica de Madrid (UPM) and her Ph.D. degree at University of Málaga
(UMA). Her research is focused on robotics and computer vision.
E.J. Pérez was born in Barcelona, Spain, in 1974. He received his title of Telecommunication Engineering from the University of Málaga,
Spain, in 1999. During 1999 he worked in a research project under a grant by the Spanish CYCIT. From 2000 to the present day
he has worked as Assistant Professor in the Department of Tecnología Electrónica of the University of Málaga. His research
is focused on robotics and artificial vision.
Javier Vázquez-Salceda is an Associate Researcher of the Artificial Intelligence Section of the Software Department (LSI), at the Technical University
of Catalonia (UPC). Javier obtained an MSc degree in Computer Science at UPC. After his master studies he became research
assistant in the KEMLg Group at UPC. In 2003 he presented his Ph.D. dissertation (with honours), which has been awarded with
the 2003 ECCAI Artificial Intelligence Dissertation Award. The dissertation has been also recently published as a book by
Birkhauser-Verlag. From 2003 to 2005 he was researcher in the Intelligent Systems Group at Utrecht University. Currently he
is again member of the KEMLg Group at UPC. His research is focused on theoretical and applied issues of Normative Systems,
software and physical agents' autonomy and social control, especially in distributed applications for complex domains such
as eCommerce or Medicine.
Miquel Sànchez-Marrè (Barcelona, 1964) received a Ph.D. in Computer Science in 1996 from the Technical University of Catalonia (UPC). He is Associate
Professor in the Computer Software Department (LSI) of the UPC since 1990 (tenure 1996). He was the head of the Artificial
Intelligence section of LSI (1997–2000). He is a pioneer member of International Environmental Modelling and Software Society
(IEMSS) and a board member of IEMSS also, since 2000. He is a member of the Editorial Board of International Journal of Applied
Intelligence, since October 2001. Since October 2004 he is Associate Editor of Environmental Modelling and Software journal.
His main research topics are case-based reasoning, machine learning, knowledge acquisition and data mining, knowledge engineering,
intelligent decision-support systems, and integrated AI architectures. He has an special interest on the application of AI
techniques to Environmental Decision Support Systems.
Francisco Sandoval was born in Spain in 1947. He received the title of Telecommunication Engineering and Ph.D. degree from the Technical University
of Madrid, Spain, in 1972 and 1980, respectively. From 1972 to 1989 he was engaged in teaching and research in the fields
of opto-electronics and integrated circuits in the Universidad Politécnica de Madrid (UPM) as an Assistant Professor and a
Lecturer successively. In 1990 he joined the University of Málaga as Full Professor in the Department of Tecnología Electrónica.
He is currently involved in autonomous systems and foveal vision, application of Artificial Neural Networks to Energy Management
Systems, and in Broad Band and Multimedia Communication. 相似文献
5.
In this paper, we propose an architecture for multimedia content delivery considering Quality of Service (QoS), based on both
the policy-based network and the best-effort network. The architecture consists of four fundamental elements: multimedia content
model, application level QoS policy, QoS adaptation mechanism, and delivery mechanism. Applications based on current architecture
loses their meaning by drastically degrading quality when network congestion occurs. Despite of this all-or-nothing architecture,
applications based on our adaptive architecture can reduce its quality and then negotiate with the network entity, keeping
its quality measure as much as possible even when network congestion occurs. We may consider a quality measure for Web pages,
total page transmission time, and transmission order of inline objects as a segregation. We then define a language to specify
application level QoS policies for Web pages and implement a delivery mechanism and a QoS adaptation mechanism to fulfill
these policies.
Kaname Harumoto, Ph.D.: He received the M.E. and Ph.D. (Eng.) degrees from Osaka University, Osaka, Japan, in 1994 and 1998, respectively. From
1994 through 1999, he was with the Department of Information Systems Engineering, Grauuate School of Engineering, Osaka University.
Since November 1999, he has been an Assistant Professor in Computation Center (currently, the name has changed to Cybermedia
Center), Osaka University. His research interests include database systems, especially in advanced network environments. He
is a member of IEEE.
Tadashi Nakano: He received the B.E. degree from Osaka University in 1999. Currently, he is a Ph.D. candidate in Graduate School of Engineering,
Osaka University. His current reeearch interests include multimedia content delivery architecture.
Shinji SHIMOJO, Ph.D.: He received the M.E. and a Dr.E. degrees from Osaka University in 1983 and 1986, respectively. From 1986 through 1989, he
was an Assistant Professor in the Department of Information and Computer Sciences, Faculty of Engineering Science, Osaka University.
From 1989 through 1998, he was an Associate Professor and since 1998, he has been a Professor in Computation Center (currently,
the name has changed to Cybermedia Center), Osaka University. He was engaged in the project of object-oriented multimedia
presentation system called Harmony. His current interests cover wide diversity of multimedia applications such as News On
Demand System, multimedia database and networked virtual reality. He is a member of ACM and IEEE. 相似文献
6.
Hypotheses constructed by inductive logic programming (ILP) systems are finite sets of definite clauses. Top-down ILP systems
usually adopt the following greedy clause-at-a-time strategy to construct such a hypothesis: start with the empty set of clauses
and repeatedly add the clause that most improves the quality of the set. This paper formulates and analyses an alternative
method for constructing hypotheses. The method, calledcautious induction, consists of a first stage, which finds a finite set of candidate clauses, and a second stage, which selects a finite subset
of these clauses to form a hypothesis. By using a less greedy method in the second stage, cautious induction can find hypotheses
of higher quality than can be found with a clause-at-a-time algorithm. We have implemented a top-down, cautious ILP system
called CILS. This paper presents CILS and compares it to Progol, a top-down clause-at-a-time ILP system. The sizes of the
search spaces confronted by the two systems are analysed and an experiment examines their performance on a series of mutagenesis
learning problems.
Simon Anthony, BEng.: Simon, perhaps better known as “Mr. Cautious” in Inductive Logic Programming (ILP) circles, completed a BEng in Information
Engineering at the University of York in 1995. He remained at York as a research student in the Intelligent Systems Group.
Concentrating on ILP, his research interests are Cautious Induction and developing number handling techniques using Constraint
Logic Programming.
Alan M. Frisch, Ph.D.: He is the Reader in Intelligent Systems at the University of York (UK), and he heads the Intelligent Systems Group in the
Department of Computer Science. He was awarded a Ph. D. in Computer Science from the University of Rochester (USA) in 1986
and has held faculty positions at the University of Sussex (UK) and the University of Illinois at Urbana-Champaign (USA).
For over 15 years Dr. Frisch has been conducting research on a wide range of topics in the area of automated reasoning, including
knowledge retrieval, probabilistic inference, constraint solving, parsing as deduction, inductive logic programming and the
integration of constraint solvers into automated deduction systems. 相似文献
7.
In this paper, we propose a reputation–oriented reinforcement learning algorithm for buying and selling agents in electronic market environments. We take into account the fact that multiple selling agents may offer the same good with different qualities. In our approach, buying agents learn to avoid the risk of purchasing low–quality goods and to maximize their expected value of goods by dynamically maintaining sets of reputable sellers. Selling agents learn to maximize their expected profits by adjusting product prices and by optionally altering the quality of their goods. Modeling the reputation of sellers allows buying agents to focus on those sellers with whom a certain degree of trust has been established. We also include the ability for buying agents to optionally explore the marketplace in order to discover new reputable sellers. As detailed in the paper, we believe that our proposed strategy leads to improved satisfaction for buyers and sellers, reduced communication load, and robust systems. In addition, we present preliminary experimental results that confirm some potential advantages of the proposed algorithm, and outline planned future experimentation to continue the evaluation of the model. 相似文献
8.
Hiroaki Imade Ryohei Morishita Isao Ono Norihiko Ono Masahiro Okamoto 《New Generation Computing》2004,22(2):177-186
In this paper, we propose a framework for enabling for researchers of genetic algorithms (GAs) to easily develop GAs running
on the Grid, named “Grid-Oriented Genetic algorithms (GOGAs)”, and actually “Gridify” a GA for estimating genetic networks,
which is being developed by our group, in order to examine the usability of the proposed GOGA framework. We also evaluate
the scalability of the “Gridified” GA by applying it to a five-gene genetic network estimation problem on a grid testbed constructed
in our laboratory.
Hiroaki Imade: He received his B.S. degree in the department of engineering from The University of Tokushima, Tokushima, Japan, in 2001.
He received the M.S. degree in information systems from the Graduate School of Engineering, The University of Tokushima in
2003. He is now in Doctoral Course of Graduate School of Engineering, The University of Tokushima. His research interests
include evolutionary computation. He currently researches a framework to easily develop the GOGA models which efficiently
work on the grid.
Ryohei Morishita: He received his B.S. degree in the department of engineering from The University of Tokushima, Tokushima, Japan, in 2002.
He is now in Master Course of Graduate School of Engineering, The University of Tokushima, Tokushima. His research interest
is evolutionary computation. He currently researches GA for estimating genetic networks.
Isao Ono, Ph.D.: He received his B.S. degree from the Department of Control Engineering, Tokyo Institute of Technology, Tokyo, Japan, in
1994. He received Ph.D. of Engineering at Tokyo Institute of Technology, Yokohama, in 1997. He worked as a Research Fellow
from 1997 to 1998 at Tokyo Institute of Technology, and at University of Tokushima, Tokushima, Japan, in 1998. He worked as
a Lecturer from 1998 to 2001 at University of Tokushima. He is now Associate Professor at University of Tokushima. His research
interests include evolutionary computation, scheduling, function optimization, optical design and bioinformatics. He is a
member of JSAI, SCI, IPSJ and OSJ.
Norihiko Ono, Ph.D.: He received his B.S. M.S. and Ph.D. of Engineering in 1979, 1981 and 1986, respectively, from Tokyo Institute of Technology.
From 1986 to 1989, he was Research Associate at Faculty of Engineering, Hiroshima University. From 1989 to 1997, he was an
associate professor at Faculty of Engineering, University of Tokushima. He was promoted to Professor in the Department of
Information Science and Intelligent Systems in 1997. His current research interests include learning in multi-agent systems,
autonomous agents, reinforcement learning and evolutionary algorithms.
Masahiro Okamoto, Ph.D.: He is currently Professor of Graduate School of Systems Life Sciences, Kyushu University, Japan. He received his Ph.D. degree
in Biochemistry from Kyushu University in 1981. His major research field is nonlinear numerical optimization and systems biology.
His current research interests cover system identification of nonlinear complex systems by using evolutional computer algorithm
of optimization, development of integrated simulator for analyzing nonlinear dynamics and design of fault-tolerant routing
network by mimicking metabolic control system. He has more than 90 peer reviewed publications. 相似文献
9.
Hyeyong Park 《New Generation Computing》2006,24(1):79-95
Since the perceptron was developed for learning to classify input patterns, there have been plenty of studies on simple perceptrons
and multilayer perceptrons. Despite wide and active studies in theory and applications, multilayer perceptrons still have
many unsettled problems such as slow learning speed and overfitting. To find a thorough solution to these problems, it is
necessary to consolidate previous studies, and find new directions for uplifting the practical power of multilayer perceptrons.
As a first step toward the new stage of studies on multilayer perceptrons, we give short reviews on two interesting and important
approaches; one is stochastic approach and the other is geometric approach. We also explain an efficient learning algorithm
developed from the statistical and geometrical studies, which is now well known as the natural gradient learning method.
Hyeyoung Park, Ph.D.: She is Assistant Professor of Computer Sciences at School of Electrical Engineering and Computer Science of Kyungpook National
University in Korea. She received her B.S., M.A. and Ph.D. from Yonsei University of Korea in 1994, 1996, and 2000. She also
worked as a research scinetist at Brain Science Institute in RIKEN from 2000 to 2004. Her research insterest is in learning
thoeries and pattern recognition as well as statistical data analysis. 相似文献
10.
We present a system for performing belief revision in a multi-agent environment. The system is called GBR (Genetic Belief
Revisor) and it is based on a genetic algorithm. In this setting, different individuals are exposed to different experiences.
This may happen because the world surrounding an agent changes over time or because we allow agents exploring different parts
of the world. The algorithm permits the exchange of chromosomes from different agents and combines two different evolution
strategies, one based on Darwin’s and the other on Lamarck’s evolutionary theory. The algorithm therefore includes also a
Lamarckian operator that changes the memes of an agent in order to improve their fitness. The operator is implemented by means
of a belief revision procedure that, by tracing logical derivations, identifies the memes leading to contradiction. Moreover,
the algorithm comprises a special crossover mechanism for memes in which a meme can be acquired from another agent only if
the other agent has “accessed” the meme, i.e. if an application of the Lamarckian operator has read or modified the meme.
Experiments have been performed on the η-queen problem and on a problem of digital circuit diagnosis. In the case of the η-queen
problem, the addition of the Lamarckian operator in the single agent case improves the fitness of the best solution. In both
cases the experiments show that the distribution of constraints, even if it may lead to a reduction of the fitness of the
best solution, does not produce a significant reduction.
Evelina Lamma, Ph.D.: She is Full Professor at the University of Ferrara. She got her degree in Electrical Engineering at the University of Bologna
in 1985, and her Ph.D. in Computer Science in 1990. Her research activity centers on extensions of logic programming languages
and artificial intelligence. She was coorganizers of the 3rd International Workshop on Extensions of Logic Programming ELP92,
held in Bologna in February 1992, and of the 6th Italian Congress on Artificial Intelligence, held in Bologna in September
1999. Currently, she teaches Artificial Intelligence and Fondations of Computer Science.
Fabrizio Riguzzi, Ph.D.: He is Assistant Professor at the Department of Engineering of the University of Ferrara, Italy. He received his Laurea from
the University of Bologna in 1995 and his Ph.D. from the University of Bologna in 1999. He joined the Department of Engineering
of the University of Ferrara in 1999. He has been a visiting researcher at the University of Cyprus and at the New University
of Lisbon. His research interests include: data mining (and in particular methods for learning from multirelational data),
machine learning, belief revision, genetic algorithms and software engineering.
Luís Moniz Pereira, Ph.D.: He is Full Professor of Computer Science at Departamento de Informática, Universidade Nova de Lisboa, Portugal. He received
his Ph.D. in Artificial Intelligence from Brunel University in 1974. He is the director of the Artificial Intelligence Centre
(CENTRIA) at Universidade Nova de Lisboa. He has been elected Fellow of the European Coordinating Committee for Artificial
Intelligence in 2001. He has been a visiting Professor at the U. California at Riverside, USA, the State U. NY at Stony Brook,
USA and the U. Bologna, Italy. His research interests include: knowledge representation, reasoning, learning, rational agents
and logic programming. 相似文献
11.
Manish Gupta Manghui Tu Latifur Khan Farokh Bastani I-Ling Yen 《Knowledge and Information Systems》2005,8(4):414-437
Advances in wireless and mobile computing environments allow a mobile user to access a wide range of applications. For example,
mobile users may want to retrieve data about unfamiliar places or local life styles related to their location. These queries
are called location-dependent queries. Furthermore, a mobile user may be interested in getting the query results repeatedly,
which is called location-dependent continuous querying. This continuous query emanating from a mobile user may retrieve information
from a single-zone (single-ZQ) or from multiple neighbouring zones (multiple-ZQ). We consider the problem of handling location-dependent
continuous queries with the main emphasis on reducing communication costs and making sure that the user gets correct current-query
result. The key contributions of this paper include: (1) Proposing a hierarchical database framework (tree architecture and
supporting continuous query algorithm) for handling location-dependent continuous queries. (2) Analysing the flexibility of
this framework for handling queries related to single-ZQ or multiple-ZQ and propose intelligent selective placement of location-dependent
databases. (3) Proposing an intelligent selective replication algorithm to facilitate time- and space-efficient processing
of location-dependent continuous queries retrieving single-ZQ information. (4) Demonstrating, using simulation, the significance
of our intelligent selective placement and selective replication model in terms of communication cost and storage constraints,
considering various types of queries.
Manish Gupta received his B.E. degree in Electrical Engineering from Govindram Sakseria Institute of Technology & Sciences, India, in
1997 and his M.S. degree in Computer Science from University of Texas at Dallas in 2002. He is currently working toward his
Ph.D. degree in the Department of Computer Science at University of Texas at Dallas. His current research focuses on AI-based
software synthesis and testing. His other research interests include mobile computing, aspect-oriented programming and model
checking.
Manghui Tu received a Bachelor degree of Science from Wuhan University, P.R. China, in 1996, and a Master's Degree in Computer Science
from the University of Texas at Dallas 2001. He is currently working toward the Ph.D. degree in the Department of Computer
Science at the University of Texas at Dallas. Mr. Tu's research interests include distributed systems, wireless communications,
mobile computing, and reliability and performance analysis. His Ph.D. research work focuses on the dependent and secure data
replication and placement issues in network-centric systems.
Latifur R. Khan has been an Assistant Professor of Computer Science department at University of Texas at Dallas since September 2000. He
received his Ph.D. and M.S. degrees in Computer Science from University of Southern California (USC) in August 2000 and December
1996, respectively. He obtained his B.Sc. degree in Computer Science and Engineering from Bangladesh University of Engineering
and Technology, Dhaka, Bangladesh, in November of 1993. Professor Khan is currently supported by grants from the National
Science Foundation (NSF), Texas Instruments, Alcatel, USA, and has been awarded the Sun Equipment Grant. Dr. Khan has more
than 50 articles, book chapters and conference papers focusing in the areas of database systems, multimedia information management
and data mining in bio-informatics and intrusion detection. Professor Khan has also served as a referee for database journals,
conferences (e.g. IEEE TKDE, KAIS, ADL, VLDB) and he is currently serving as a program committee member for the 11th ACM SIGKDD
International Conference on Knowledge Discovery and Data Mining (SIGKDD2005), ACM 14th Conference on Information and Knowledge
Management (CIKM 2005), International Conference on Database and Expert Systems Applications DEXA 2005 and International Conference
on Cooperative Information Systems (CoopIS 2005), and is program chair of ACM SIGKDD International Workshop on Multimedia
Data Mining, 2004.
Farokh Bastani received the B.Tech. degree in Electrical Engineering from the Indian Institute of Technology, Bombay, and the M.S. and Ph.D.
degrees in Computer Science from the University of California, Berkeley. He is currently a Professor of Computer Science at
the University of Texas at Dallas. Dr. Bastani's research interests include various aspects of the ultrahigh dependable systems,
especially automated software synthesis and testing, embedded real-time process-control and telecommunications systems and
high-assurance systems engineering.
Dr. Bastani was the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (IEEE-TKDE). He is currently
an emeritus EIC of IEEE-TKDE and is on the editorial board of the International Journal of Artificial Intelligence Tools,
the International Journal of Knowledge and Information Systems and the Springer-Verlag series on Knowledge and Information
Management. He was the program cochair of the 1997 IEEE Symposium on Reliable Distributed Systems, 1998 IEEE International
Symposium on Software Reliability Engineering, 1999 IEEE Knowledge and Data Engineering Workshop, 1999 International Symposium
on Autonomous Decentralised Systems, and the program chair of the 1995 IEEE International Conference on Tools with Artificial
Intelligence. He has been on the program and steering committees of several conferences and workshops and on the editorial
boards of the IEEE Transactions on Software Engineering, IEEE Transactions on Knowledge and Data Engineering and the Oxford
University Press High Integrity Systems Journal.
I-Ling Yen received her B.S. degree from Tsing-Hua University, Taiwan, and her M.S. and Ph.D. degrees in Computer Science from the University
of Houston. She is currently an Associate Professor of Computer Science at University of Texas at Dallas. Dr. Yen's research
interests include fault-tolerant computing, security systems and algorithms, distributed systems, Internet technologies, E-commerce
and self-stabilising systems. She has published over 100 technical papers in these research areas and received many research
awards from NSF, DOD, NASA and several industry companies. She has served as Program Committee member for many conferences
and Program Chair/Cochair for the IEEE Symposium on Application-Specific Software and System Engineering & Technology, IEEE
High Assurance Systems Engineering Symposium, IEEE International Computer Software and Applications Conference, and IEEE International
Symposium on Autonomous Decentralized Systems. She has also served as a guest editor for a theme issue of IEEE Computer devoted
to high-assurance systems. 相似文献
12.
Mining user access patterns with traversal constraint for predicting web page requests 总被引:4,自引:4,他引:0
Mei-Ling Shyu Choochart Haruechaiyasak Shu-Ching Chen 《Knowledge and Information Systems》2006,10(4):515-528
The recent increase in HyperText Transfer Protocol (HTTP) traffic on the World Wide Web (WWW) has generated an enormous amount of log records on Web server databases. Applying Web mining techniques on these server log records can discover potentially useful patterns and reveal user access behaviors on the Web site. In this paper, we propose a new approach for mining user access patterns for predicting Web page requests, which consists of two steps. First, the Minimum Reaching Distance (MRD) algorithm is applied to find the distances between the Web pages. Second, the association rule mining technique is applied to form a set of predictive rules, and the MRD information is used to prune the results from the association rule mining process. Experimental results from a real Web data set show that our approach improved the performance over the existing Markov-model approach in precision, recall, and the reduction of user browsing time.
Mei-Ling Shyu received her Ph.D. degree from the School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN in 1999, and three Master's degrees from Computer Science, Electrical Engineering, and Restaurant, Hotel, Institutional, and Tourism Management from Purdue University. She has been an Associate Professor in the Department of Electrical and Computer Engineering (ECE) at the University of Miami (UM), Coral Gables, FL, since June 2005, Prior to that, she was an Assistant Professor in ECE at UM dating from January 2000. Her research interests include data mining, multimedia database systems, multimedia networking, database systems, and security. She has authored and co-authored more than 120 technical papers published in various prestigious journals, refereed conference/symposium/workshop proceedings, and book chapters. She is/was the guest editor of several journal special issues.
Choochart Haruechaiyasak received his Ph.D. degree from the Department of Electrical and Computer Engineering, University of Miami, in 2003 with the Outstanding Departmental Graduating Student award from the College of Engineering. After receiving his degree, he has joined the National Electronics and Computer Technology Center (NECTEC), located in Thailand Science Park, as a researcher in Information Research and Development Division (RDI). His current research interests include data/ text/ Web mining, Natural Language Processing, Information Retrieval, Search Engines, and Recommender Systems. He is currently leading a small group of researchers and programmer to develop an open-source search engine for Thai language. One of his objectives is to promote the use of data mining technology and other advanced applications in Information Technology in Thailand. He is also a visiting lecturer for Data Mining, Artificial Intelligence and Decision Support Systems courses in many universities in Thailand.
Shu-Ching Chen received his Ph.D. from the School of Electrical and Computer Engineering at Purdue University, West Lafayette, IN, USA in December, 1998. He also received Master's degrees in Computer Science, Electrical Engineering, and Civil Engineering from Purdue University. He has been an Associate Professor in the School of Computing and Information Sciences (SCIS), Florida International University (FIU) since August, 2004. Prior to that, he was an Assistant Professor in SCIS at FIU dating from August, 1999. His main research interests include distributed multimedia database systems and multimedia data mining. Dr. Chen has authored and co-authored more than 140 research papers in journals, refereed conference/symposium/workshop proceedings, and book chapters. In 2005, he was awarded the IEEE Systems, Man, and Cybernetics Society's Outstanding Contribution Award. He was also awarded a University Outstanding Faculty Research Award from FIU in 2004, Outstanding Faculty Service Award from SCIS in 2004 and Outstanding Faculty Research Award from SCIS in 2002. 相似文献
13.
14.
A database session is a sequence of requests presented to the database system by a user or an application to achieve a certain task. Session identification is an important step in discovering useful patterns from database trace logs. The discovered patterns can be used to improve the performance of database systems by prefetching predicted queries, rewriting the current query or conducting effective cache replacement.In this paper, we present an application of a new session identification method based on statistical language modeling to database trace logs. Several problems of the language modeling based method are revealed in the application, which include how to select values for the parameters of the language model, how to evaluate the accuracy of the session identification result and how to learn a language model without well-labeled training data. All of these issues are important in the successful application of the language modeling based method for session identification. We propose solutions to these open issues. In particular, new methods for determining an entropy threshold and the order of the language model are proposed. New performance measures are presented to better evaluate the accuracy of the identified sessions. Furthermore, three types of learning methods, namely, learning from labeled data, learning from semi-labeled data and learning from unlabeled data, are introduced to learn language models from different types of training data. Finally, we report experimental results that show the effectiveness of the language model based method for identifying sessions from the trace logs of an OLTP database application and the TPC-C Benchmark.
Xiangji Huang joined York University as an Assistant Professor in July 2003 and then became a tenured Associate Professor in May 2006. Previously, he was a Post Doctoral Fellow at the School of Computer Science, University of Waterloo, Canada. He did his Ph.D. in Information Science at City University in London, England, with Professor Stephen E. Robertson. Before he went into his Ph.D. program, he worked as a lecturer for 4 years at Wuhan University. He also worked in the financial industry in Canada doing E-business, where he was awarded a CIO Achievement Award, for three and half years. He has published more than 50 refereed papers in journals, book chapter and conference proceedings. His Master (M.Eng.) and Bachelor (B.Eng.) degrees were in Computer Organization & Architecture and Computer Engineering, respectively. His research interests include information retrieval, data mining, natural language processing, bioinformatics and computational linguistics.
Qingsong Yao is a Ph.D. student in the Department of Computer Science and Engineering at York University, Toronto, Canada. His research interests include database management systems and query optimization, data mining, information retrieval, natural language processing and computational linguistics. He earned his Master's degree in Computer Science from Institute of Software, Chinese Academy of Science in 1999 and Bachelor's degree in Computer Science from Tsinghua University.
Aijun An is an associate professor in the Department of Computer Science and Engineering at York University, Toronto, Canada. She received her Bachelor's and Master's degrees in Computer Science from Xidian University in China. She received her PhD degree in Computer Science from the University of Regina in Canada in 1997. She worked at the University of Waterloo as a postdoctoral fellow from 1997 to 1999 and as a research assistant professor from 1999 to 2001. She joined York University in 2001. She has published more than 60 papers in refereed journals and conference proceedings. Her research interests include data mining, machine learning, and information retrieval. 相似文献
15.
This article describes the issues in multiagent learning towards RoboCup,1≈3) especially for the real robot leagues. First, the review of the issue in the context of the related area is given, then related
works from several viewpoints are reviewed. Next, our approach towards RoboCup Initiative is introduced and finally future
issues are given.
Minoru Asada, Ph.D.: He received B.E., M.Sc., and Ph.D., degrees in control engineering from Osaka University, in 1977, 1979, and 1982, respectively.
From 1982 to 1988, he was a research associate of Control Engineering, Osaka University. In 1989, he became an associate professor
of Mechanical Engineering for Computer-Controlled Machinery, Osaka University. In 1995 he became a professor of the department
of Adaptive Machine Systems at the same university. From 1986 to 1987, he was a visiting researcher of Center for Automation
Research, University of Maryland, College Park, MD.
He received the 1992 best paper award of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS92), and
the 1996 best paper award of RSJ (Robotics Society of Japan). Also, his paper was one of the finalists of IEEE Robotics and
Automation Society 1995 Best Conference Paper Award. He was a general chair of IEEE/RSJ 1996 International Conference on Intelligent
Robots and Systems (IROS96). Since early 1990, he has been involved in RoboCup activities and his team was the first champion
team with USC team in the middle size league of the first RoboCup held in conjunction with IJCAI-97, Nagoya, Japan.
Eiji Uchibe, Ph.D.: He received a Ph.D. degree in mechanical engineering from Osaka University in 1999. He is currently a research associate
of the Japan Society for the Promotion of Science, in Research for the Future Program titled Cooperative Distributed Vision
for Dynamic Three Dimensional Scene Understanding. His research interests are in reinforcement learning, evolutionary computation,
and their applications. He is a member of IEEE, AAAI, RSJ, and JSAI. 相似文献
16.
STAMP: A Model for Generating Adaptable Multimedia Presentations 总被引:1,自引:1,他引:0
The STAMP model addresses the dynamic generation of multimedia presentations in the domain of Multimedia Web-based Information Systems. STAMP allows the presentation of multimedia data obtained from XML compatible data sources by means of query. Assuming that the size and the nature of the elements of information provided by a data source is not known a priori, STAMP proposes templates which describe the spatial, temporal, navigational structuration of multimedia presentations whose content varies. The instantiation of a template is done with respect to the set of spatial and temporal constraints associated with the delivery context. A set of adaptations preserving the initial intention of the presentation is proposed.Ioan Marius Bilasco is a Ph.D. student at the University Joseph Fourier in Grenoble, France, since 2003. He received his BS degree in Computer Science form the University Babes Bolyai in Cluj-Napoca, Romania and his MS degree in Computer Science from the University Joseph Fourier in Grenoble, France. He joined the LSR-IMAG Laboratory in Grenoble in 2001. His research interests include adaptability in Web-based Information Systems, 3D multimedia data modelling and mobile communications.Jérôme Gensel is an Assistant Professor at the University Pierre Mendès France in Grenoble, France, since 1996. He received his Ph.D. in 1995 from the University of Grenoble for his work on Constraint Programming and Knowledge Representation in the Sherpa project at the French National Institute of Computer Sciences and Automatics (INRIA). He joined the LSR-IMAG Laboratory in Grenoble in 2001. His research interests include adaptability and cooperation in Web-based Information Systems, multimedia data (especially video) modeling, semi-structured and object-based knowledge representation and constraint programming.Marlène Villanova-Oliver is an Assistant Professor at the University Pierre Mendès France in Grenoble, France, since 2003. In 1999, she received her MS degree in Computer Science from the University Joseph Fourier of Grenoble and the European Diploma of 3rd cycle in Management and Technology of Information Systems (MATIS). She received her Ph.D. in 2002 from the National Polytechnic Institute of Grenoble (INPG). She is a member of the LSR-IMAG Laboratory in Grenoble since 1998. Her research interests include adaptability in Web-based Information Systems, user modeling, adaptable Web Services. 相似文献
17.
Qianhui Althea Liang Jen-Yao Chung Steven Miller 《Knowledge and Information Systems》2007,13(3):367-394
When meeting the challenges in automatic and semi-automatic Web service composition, capturing the user’s service demand and
preferences is as important as knowing what the services can do. This paper discusses the idea of semantic service requests
for composite services, and presents a multi-attribute utility theory (MAUT) based model of composite service requests. Service
requests are modeled as user preferences and constraints. Two preference structures, additive independence and generalized
additive independence, are utilized in calculating the expected utilities of service composition outcomes. The model is also
based on an iterative and incremental scheme meant to better capture requirements in accordance with service consumers’ needs.
OWL-S markup vocabularies and associated inference mechanism are used as a means to bring semantics to service requests. Ontology
conceptualizations and language constructs are added to OWL-S as uniform representations of possible aspects of the requests.
This model of semantics in service requests enables unambiguous understanding of the service needs and more precise generation
of the desired compositions. An application scenario is presented to illustrate how the proposed model can be applied in the
real business world.
Qianhui Althea Liang received her Ph.D from the Department of Electrical and Computer Engineering, University of Florida in 2004. While pursuing
her Ph.D, she was a member of Database Systems Research and Development Center at the University of Florida. She received
both her bachelor’s and master’s from the Department of Computer Science and Engineering, Zhejiang University, China. She
joined the School of Information Systems at Singapore Management University, Singapore, as an assistant professor in 2005.
Her major research interests are service composition, dynamic service discovery, multimedia Web services, and applied artificial
intelligence.
Jen-Yao Chung received the M.S. and Ph.D degrees in computer science from the University of Illinois at Urbana-Champaign. Currently, he
is the senior manager for Engineering and Technology Services Innovation, where he was responsible for identifying and creating
emergent solutions. He was Chief Technology Officer for IBM Global Electronics Industry. Before that, he was program director
for IBM Institute for Advanced Commerce Technology office. He is the co-founder of IEEE technical committee on e-Commerce
(TCEC). He has served as general chair and program chair for many international conferences, most recently he served as the
steering committee chair for the IEEE International Conference on e-Commerce Technology (CEC06) and general chair for the
IEEE International Conference on e-Business Engineering (ICEBE06). He has authored or coauthored over 150 technical papers
in published journals or conference proceedings. He is a senior member of the IEEE and a member of ACM.
Miller is founding Dean of the School of Information Systems (SIS) at Singapore Management University, and also serves as Practice
Professor of Information Systems. Since 2003, he has led efforts to launch and establish the undergraduate, graduate and professional
programs of the SIS. Immediately prior to joining SMU, Dr. Miller served as Chief Architect Executive for the Business Consulting
Services unit of IBM Global Services in Asia Pacific. He held prior industry appointments with Fujitsu Network Systems, and
with RWD Technologies. Dr. Miller started his professional career as an Assistant Professor at Carnegie Mellon University,
conducting research and teaching related to Computer-Integrated Manufacturing and Robotics applications and impacts. He has
a Bachelors of Engineering Degree in Systems Engineering (Magna Cum Laude) from the University of Pennsylvania and a Masters
of Science in Statistics and a Ph.D in Engineering and Public Policy from Carnegie Mellon University. 相似文献
18.
Tao Li Lei Guo Lingyao Wu Changyin Sun 《International Journal of Control, Automation and Systems》2009,7(2):281-287
This article investigates the problem of robust stability for neural networks with time-varying delays and parameter uncertainties
of linear fractional form. By introducing a new Lyapunov-Krasovskii functional and a tighter inequality, delay-dependent stability
criteria are established in term of linear matrix inequalities (LMIs). It is shown that the obtained criteria can provide
less conservative results than some existing ones. Numerical examples are given to demonstrate the applicability of the proposed
approach.
Recommended by Editorial Board member Naira Hovakimyan under the direction of Editor Young-Hoon Joo. This work was supported
by the National Science foundation of China under Grant no. 60774013 and Key Laboratory of Education Ministry for Image Processing
and Intelligent Control under grant no. 200805.
Tao Li received the Ph.D. degree in The Research Institute of Automation Southeast University, China. Now He is an Assistant Professor
in Department of Information and Communication, Nanjing University of Information Science and Technology. His current research
interests include time-delay systems, neural networks, robust control, fault detection and diagnosis.
Lei Guo received the Ph.D. degree in the Research Institute of Automation Southeast University, China. From 1999 to 2004, he has
worked at Hong Kong University, IRCCyN (France), Glasgow University, Loughborough University and UMIST, UK. Now He is a Professor
in School of Instrument Science and Opto-Electronics Engineering, Beihang University. His current research interests include
robust control, fault detection and diagnosis.
Lingyao Wu received the Ph.D. degree in The Research Institute of Automation Southeast University, China. Now He is an Assistant Professor
in the Research Institute of Automation Southeast University. His current research interests include time-delay systems, neural
networks, robust control, fault detection and diagnosis.
Changyin Sun received the Ph.D. degree in the Research Institute of Automation Southeast University, China. Now He is a Professor in the
Research Institute of Automation Southeast University. His current research interests include timedelay systems, neural networks. 相似文献
19.
Thomas Tran 《Knowledge and Information Systems》2010,22(1):65-100
In this paper, we describe a framework for modelling the trustworthiness of sellers in the context of an electronic marketplace
where multiple selling agents may offer the same good with different qualities and selling agents may alter the quality of
their goods. We consider that there may be dishonest sellers in the market (for example, agents who offer goods with high
quality and later offer the same goods with very low quality). In our approach, buying agents use a combination of reinforcement
learning and trust modelling to enhance their knowledge about selling agents and hence their opportunities to purchase high
value goods in the marketplace. This paper focuses on presenting the theoretical results demonstrating how the modelling of
trust can protect buying agents from dishonest selling agents. The results show that our proposed buying agents will not be
harmed infinitely by dishonest selling agents and therefore will not incur infinite loss, if they are cautious in setting
their penalty factor. We also discuss the value of our particular model for trust, in contrast with related work and conclude
with directions for future research. 相似文献
20.
Decision tree (DT) induction is among the more popular of the data mining techniques. An important component of DT induction
algorithms is the splitting method, with the most commonly used method being based on the Conditional Entropy (CE) family.
However, it is well known that there is no single splitting method that will give the best performance for all problem instances.
In this paper we explore the relative performance of the Conditional Entropy family and another family that is based on the
Class-Attribute Mutual Information (CAMI) measure. Our results suggest that while some datasets are insensitive to the choice
of splitting methods, other datasets are very sensitive to the choice of splitting methods. For example, some of the CAMI
family methods may be more appropriate than the popular Gain Ratio (GR) method for datasets which have nominal predictor attributes,
and are competitive with the GR method for those datasets where all predictor attributes are numeric. Given that it is never
known beforehand which splitting method will lead to the best DT for a given dataset, and given the relatively good performance
of the CAMI methods, it seems appropriate to suggest that splitting methods from the CAMI family should be included in data
mining toolsets.
Kweku-Mauta Osei-Bryson is Professor of Information Systems at Virginia Commonwealth University, where he also served as the Coordinator of the Ph.D.
program in Information Systems during 2001–2003. Previously he was Professor of Information Systems and Decision Analysis
in the School of Business at Howard University, Washington, DC, U.S.A. He has also worked as an Information Systems practitioner
in both industry and government. He holds a Ph.D. in Applied Mathematics (Management Science & Information Systems) from the
University of Maryland at College Park, a M.S. in Systems Engineering from Howard University, and a B.Sc. in Natural Sciences
from the University of the West Indies at Mona. He currently does research in various areas including: Data Mining, Expert
Systems, Decision Support Systems, Group Support Systems, Information Systems Outsourcing, Multi-Criteria Decision Analysis.
His papers have been published in various journals including: Information & Management, Information Systems Journal, Information
Systems Frontiers, Business Process Management Journal, International Journal of Intelligent Systems, IEEE Transactions on
Knowledge & Data Engineering, Data & Knowledge Engineering, Information & Software Technology, Decision Support Systems, Information
Processing and Management, Computers & Operations Research, European Journal of Operational Research, Journal of the Operational
Research Society, Journal of the Association for Information Systems, Journal of Multi-Criteria Decision Analysis, Applications
of Management Science. Currently he serves an Associate Editor of the INFORMS Journal on Computing, and is a member of the
Editorial Board of the Computers & Operations Research journal.
Kendall E. Giles received the BS degree in Electrical Engineering from Virginia Tech in 1991, the MS degree in Electrical Engineering from
Purdue University in 1993, the MS degree in Information Systems from Virginia Commonwealth University in 2002, and the MS
degree in Computer Science from Johns Hopkins University in 2004. Currently he is a PhD student (ABD) in Computer Science
at Johns Hopkins, and is a Research Assistant in the Applied Mathematics and Statistics department. He has over 15 years of
work experience in industry, government, and academic institutions. His research interests can be partially summarized by
the following keywords: network security, mathematical modeling, pattern classification, and high dimensional data analysis. 相似文献