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101.
William McCune 《Journal of Automated Reasoning》1992,9(2):147-167
This article addresses the problem of indexing and retrieving first-order predicate calculus terms in the context of automated deduction programs. The four retrieval operations of concern are to find variants, generalizations, instances, and terms that unify with a given term. Discrimination-tree indexing is reviewed, and several variations are presented. The path-indexing method is also reviewed. Experiments were conducted on large sets of terms to determine how the properties of the terms affect the performance of the two indexing methods. Results of the experiments are presented.This was supported by the Applied Mathematical Sciences subprogram of the Office of Energy Research, U.S. Department of Energy, under Contract W-31-109-Eng-38. 相似文献
102.
Rong Zhao 《Pattern recognition》2002,35(3):593-600
In this paper, we present the results of a project that seeks to transform low-level features to a higher level of meaning. This project concerns a technique, latent semantic indexing (LSI), in conjunction with normalization and term weighting, which have been used for full-text retrieval for many years. In this environment, LSI determines clusters of co-occurring keywords, sometimes, called concepts, so that a query which uses a particular keyword can then retrieve documents perhaps not containing this keyword, but containing other keywords from the same cluster. In this paper, we examine the use of this technique for content-based image retrieval, using two different approaches to image feature representation. We also study the integration of visual features and textual keywords and the results show that it can help improve the retrieval performance significantly. 相似文献
103.
本文根据特征平行四边形法和N/(1/2)比值法提出了一种简便快速的电子衍射谱分析方法——图表法,由于它以一些简单的操作取代了常用分析法的数据测量和计算过程,因此具有简便、快速、可靠性高等优点。 相似文献
104.
Bertrand Meyer 《Information Processing Letters》1985,21(5):219-227
The problem studied in this paper is to search a given text for occurrences of certain strings, in the particular case where the set of strings may change as the search proceeds.A well-known algorithm by Aho and Corasick applies to the simpler case when the set of strings is known beforehand and does not change. This algorithm builds a transition diagram (finite automaton) from the strings, and uses it as a guide to traverse the text. The search can then be done in linear time.We show how this algorithm can be modified to allow incremental diagram construction, so that new keywords may be entered at any time during the search. The incremental algorithm presented essentially retains the time and space complexities of the non-incremental one. 相似文献
105.
106.
Roberto Grossi 《Theoretical computer science》2011,412(27):2964-2973
Suffix arrays are a key data structure for solving a run of problems on texts and sequences, from data compression and information retrieval to biological sequence analysis and pattern discovery. In their simplest version, they can just be seen as a permutation of the elements in {1,2,…,n}, encoding the sorted sequence of suffixes from a given text of length n, under the lexicographic order. Yet, they are on a par with ubiquitous and sophisticated suffix trees. Over the years, many interesting combinatorial properties have been devised for this special class of permutations: for instance, they can implicitly encode extra information, and they are a well characterized subset of the n! permutations. This paper gives a short tutorial on suffix arrays and their compressed version to explore and review some of their algorithmic features, discussing the space issues related to their usage in text indexing, combinatorial pattern matching, and data compression. 相似文献
107.
108.
Feature selection for text categorization is a well-studied problem and its goal is to improve the effectiveness of categorization, or the efficiency of computation, or both. The system of text categorization based on traditional term-matching is used to represent the vector space model as a document; however, it needs a high dimensional space to represent the document, and does not take into account the semantic relationship between terms, which leads to a poor categorization accuracy. The latent semantic indexing method can overcome this problem by using statistically derived conceptual indices to replace the individual terms. With the purpose of improving the accuracy and efficiency of categorization, in this paper we propose a two-stage feature selection method. Firstly, we apply a novel feature selection method to reduce the dimension of terms; and then we construct a new semantic space, between terms, based on the latent semantic indexing method. Through some applications involving the spam database categorization, we find that our two-stage feature selection method performs better. 相似文献
109.
Akila Varadarajan Nilesh Patel Bruce Maxim William I. Grosky 《Multimedia Tools and Applications》2008,40(2):211-239
There has been increased interest on the impact of mobile devices such as PDAs and Tablet PCs in introducing new pedagogical
approaches and active learning experiences. We propose an intelligent system that efficiently addresses the inherent subjectivity
in student perception of note taking and information retrieval. We employ the idea of cross indexing the digital ink notes
with matching electronic documents in the repository. Latent Semantic Indexing is used to perform document and page level
indexing. Thus for each retrieved document, the user can go over to the relevant pages that match the query. Techniques to
handle problems such as polysemy (multiple meanings of a word) in large databases, document folding and no match for query
are discussed. We tested our system for its performance, usability and effectiveness in the learning process. The results
from the exploratory studies reveal that the proposed system provides a highly enhanced student learning experience, thereby
facilitating high test scores.
Akila Varadarajan is a Senior Software Engineer at Motorola, IL with the Mobile devices division. Prior joining Motorola, she was a Software development intern at Autodesk, MI and Graduate Research assistant at University of Michigan - Dearborn. She received her MS in Computer Engineering from University of Michigan in 2006 and her BS in Computer Engineering from Madurai Kamaraj University, India in 2003. She is interested in Mobile computing - specifically Human Factors of Mobile Computing, Information retrieval and pattern recognition. Nilesh Patel is Assistant Professor in the department of Computer Science and Engineering at Oakland University, MI. He received his PhD and MS in Computer Science from Wayne State University, MI in 1997 and 1993. He is interested in Multimedia Information Processing - specifically audio and video indexing, retrieval and event detection, Pattern Recognition, Distributed Data Mining in a heterogeneous environment, and Computer Vision with special interest in medical imaging. Dr. Patel has also served in the automotive sector for several years and developed interest in Telematics and Mobile Computing. Bruce Maxim has worked as a software engineer for the past 31 years. He is a member of the Computer and Information Science faculty at the University of Michigan-Dearborn since 1985. He serves as the computing laboratory supervisor and head of the undergraduate programs in Computer Science, Software Engineering, and Information Systems. He has created more than 15 Computer and Information Science courses dealing with software engineering, game design, artificial intelligence, user interface design, web engineering, software quality, and computer programming. He has authored or co-authored four books on programming and software engineering. He has most recently served on the pedagogy subcommittee for Software Engineering 2004 and contributed to the IDGA Game Curriculum Framework 2008 guidelines. William I. Grosky is currently Professor and Chair of the Department of Computer and Information Science at University of Michigan - Dearborn, Dearborn, Michigan. Prior to joining the University of Michigan in 2001, he was Professor and Chair of the Department of Computer Science at Wayne State University, Detroit, Michigan. Before joining Wayne State University in 1976, he was an Assistant Professor in the Department of Information and Computer Science at Georgia Tech, Atlanta, Georgia. He received his B.S. in Mathematics from MIT in 1965, his M.S. in Applied Mathematics from Brown University in 1968, and his Ph.D. in Engineering and Applied Science from Yale University in 1971. 相似文献
William I. GroskyEmail: |
Akila Varadarajan is a Senior Software Engineer at Motorola, IL with the Mobile devices division. Prior joining Motorola, she was a Software development intern at Autodesk, MI and Graduate Research assistant at University of Michigan - Dearborn. She received her MS in Computer Engineering from University of Michigan in 2006 and her BS in Computer Engineering from Madurai Kamaraj University, India in 2003. She is interested in Mobile computing - specifically Human Factors of Mobile Computing, Information retrieval and pattern recognition. Nilesh Patel is Assistant Professor in the department of Computer Science and Engineering at Oakland University, MI. He received his PhD and MS in Computer Science from Wayne State University, MI in 1997 and 1993. He is interested in Multimedia Information Processing - specifically audio and video indexing, retrieval and event detection, Pattern Recognition, Distributed Data Mining in a heterogeneous environment, and Computer Vision with special interest in medical imaging. Dr. Patel has also served in the automotive sector for several years and developed interest in Telematics and Mobile Computing. Bruce Maxim has worked as a software engineer for the past 31 years. He is a member of the Computer and Information Science faculty at the University of Michigan-Dearborn since 1985. He serves as the computing laboratory supervisor and head of the undergraduate programs in Computer Science, Software Engineering, and Information Systems. He has created more than 15 Computer and Information Science courses dealing with software engineering, game design, artificial intelligence, user interface design, web engineering, software quality, and computer programming. He has authored or co-authored four books on programming and software engineering. He has most recently served on the pedagogy subcommittee for Software Engineering 2004 and contributed to the IDGA Game Curriculum Framework 2008 guidelines. William I. Grosky is currently Professor and Chair of the Department of Computer and Information Science at University of Michigan - Dearborn, Dearborn, Michigan. Prior to joining the University of Michigan in 2001, he was Professor and Chair of the Department of Computer Science at Wayne State University, Detroit, Michigan. Before joining Wayne State University in 1976, he was an Assistant Professor in the Department of Information and Computer Science at Georgia Tech, Atlanta, Georgia. He received his B.S. in Mathematics from MIT in 1965, his M.S. in Applied Mathematics from Brown University in 1968, and his Ph.D. in Engineering and Applied Science from Yale University in 1971. 相似文献
110.
Genomic sequence comparison algorithms represent the basic toolbox for processing large volume of DNA or protein sequences.
They are involved both in the systematic scan of databases, mostly for detecting similarities with an unknown sequence, and
in preliminary processing before advanced bioinformatics analysis. Due to the exponential growth of genomic data, new solutions
are required to keep the computation time reasonable. This paper presents a specific hardware architecture to speed-up seed-based
algorithms which are currently the most popular heuristics for detecting alignments. The architecture regroups FLASH and FPGA
technologies on a common support, allowing a large amount of data to be rapidly accessed and quickly processed. Experiments
on database search and intensive sequence comparison demonstrate a good cost/performance ratio compared to standard approaches.
相似文献
D. LavenierEmail: |