Regularized least squares support vector regression for the simultaneous learning of a function and its derivatives

In this paper, we propose a regularized least squares approach based support vector machine for simultaneously approximating a function and its derivatives. The proposed algorithm is simple and fast as no quadratic programming solver needs to be employed. Effectively, only the solution of a structured system of linear equations is needed.     

An ensemble of support vector machines for predicting virulent proteins

It is important to develop a reliable system for predicting bacterial virulent proteins for finding novel drug/vaccine and for understanding virulence mechanisms in pathogens.In this work we have proposed a bacterial virulent protein prediction method based on an ensemble of classifiers where the features are extracted directly from the amino acid sequence of a given protein. It is well known in the literature that the features extracted from the evolutionary information of a given protein are better than the features extracted from the amino acid sequence. Our method tries to fill the gap between the amino acid sequence based approaches and the evolutionary information based approaches.An extensive evaluation according to a blind testing protocol, where the parameters of the system are calculated using the training set and the system is validated in three different independent datasets, has demonstrated the validity of the proposed method.     

A decision support system for the automatic management of keep-clear signs based on support vector machines and geographic information systems

This paper presents a decision support system for automatic keep-clear signs management. The system consists of several modules. First of all, an acquisition module obtains images using a vehicle equipped with two recording cameras. A recognition module, which is based on Support Vector Machines (SVMs), analyzes each image and decides if there is a keep-clear sign in it. The images with keep-clear signs are included into a Geographical Information System (GIS) database. Finally in the management module, the data in the GIS are compared with the council database in order to decide actions such as repairing or reposition of signs, detection of possible frauds etc. We present the first tests of the system in a Spanish city (Meco, Madrid), where the systems is being tested for its application in the near future.     

Variant of Gaussian kernel and parameter setting method for nonlinear SVM

The classification problem by the nonlinear support vector machine (SVM) with kernel function is discussed in this paper. Firstly, the stretching ratio is defined to analyze the performance of the kernel function, and a new type of kernel function is introduced by modifying the Gaussian kernel. The new kernel function has many properties as good as or better than Gaussian kernel: such as its stretching ratio is always lager than 1, and its implicit kernel map magnifies the distance between the vectors in local but without enlarging the radius of the circumscribed hypersphere that includes the whole mapping vectors in feature space, which maybe gets a bigger margin. Secondly, two aspects are considered to choose a good spread parameter for a given kernel function approximately and easily. One is the distance criterion which minimizes the sum-square distance between the labeled training sample and its own center and maximizes the sum-square distance between the training sample and the other labeled-center, which is equivalent to the famous Fisher ratio. The other is the angle criterion which minimizes the angle between the kernel matrix and the target matrix. Then a better criterion is given by combined those aspects. Finally, some experiments show that our methods are efficient.     

Method for prediction of protein-protein interactions in yeast using genomics/proteomics information and feature selection

Protein-protein interaction (PPI) prediction is one of the main goals in the current Proteomics. This work presents a method for prediction of protein-protein interactions through a classification technique known as support vector machines. The dataset considered is a set of positive and negative examples taken from a high reliability source, from which we extracted a set of genomic features, proposing a similarity measure. From this dataset we extracted 26 proteomics/genomics features using well-known databases and datasets. Feature selection was performed to obtain the most relevant variables through a modified method derived from other feature selection methods for classification. Using the selected subset of features, we constructed a support vector classifier that obtains values of specificity and sensitivity higher than 90% in prediction of PPIs, and also providing a confidence score in interaction prediction of each pair of proteins.     

Generalization performance of support vector machines and neural networks in runoff modeling

Effective one-day lead runoff prediction is one of the significant aspects of successful water resources management in arid region. For instance, reservoir and hydropower systems call for real-time or on-line site-specific forecasting of the runoff. In this research, we present a new data-driven model called support vector machines (SVMs) based on structural risk minimization principle, which minimizes a bound on a generalized risk (error), as opposed to the empirical risk minimization principle exploited by conventional regression techniques (e.g. ANNs). Thus, this stat-of-the-art methodology for prediction combines excellent generalization property and sparse representation that lead SVMs to be a very promising forecasting method. Further, SVM makes use of a convex quadratic optimization problem; hence, the solution is always unique and globally optimal. To demonstrate the aforementioned forecasting capability of SVM, one-day lead stream flow of Bakhtiyari River in Iran was predicted using the local climate and rainfall data. Moreover, the results were compared with those of ANN and ANN integrated with genetic algorithms (ANN-GA) models. The improvements in root mean squared error (RMSE) and squared correlation coefficient (R²) by SVM over both ANN models indicate that the prediction accuracy of SVM is at least as good as that of those models, yet in some cases actually better, as well as forecasting of high-value discharges.     

基于N元汉字串模型的文本表示和实时分类的研究与实现

该文提出了一种基于N元汉字串特征的文本向量空间表示模型,用这个表示模型实现了一个文本实时分类系统。对比使用词语做为特征的文本向量空间模型,这种新的模型由于使用快速的多关键词匹配技术,不使用分词等复杂计算,可以实现实时文本分类。由于N元汉字串的文本表示模型中的特征抽取中不需要使用词典分词,从而可以提取出一些非词的短语结构,在特殊的应用背景,如网络有害信息判别中,能自动提取某些更好的特征项。实验结果表明,使用简单的多关键词匹配和使用复杂的分词,对分类系统的效果影响是很小的。该文的研究表明N元汉字串特征和词特征的表示能力在分类问题上基本是相同的,但是N元汉字串特征的分类系统可以比分词系统的性能高出好几倍。该文还描述了使用这种模型的自动文本分类系统,包括分类系统的结构,特征提取,文本相似度计算公式,并给出了评估方法和实验结果。     

Individualist and collectivist perspectives on knowledge in organizations: Implications for information systems research

Organization scholars differ in their understanding and application of the construct of “knowledge” in theorizing and empirical research. Over the past years, two perspectives have become prevalent in organization science. The individualist perspective assumes the locus of knowledge is people who learn, and that knowledge cannot extend beyond the physical limits of human beings. The collectivist perspective assumes the locus of knowledge is collective. Collective entities accumulate knowledge through forms of social learning. Boundaries of knowledge are drawn around social entities—groups, communities, networks, and organizational units, etc. Recent work in management and organization science has accentuated the differences, and argued against the widespread adoption of a collectivist perspective. This argument holds implications for information systems research. The current paper reviews selected contributions on the locus of knowledge, presents an argument for a combined collectivist and individualist perspective, and outlines future directions for information systems research. Drawing on two significant examples, I show that information systems research has a strategic role to play in greatly advancing this combined perspective.     

开放式软件中中文信息按部件检索的设计与实现

利用汉字数学表达式的思想,将汉字数学表达式库嵌入到开放式软件中,用来弥补汉字内码中包含信息量不足的缺点,使计算机能以比汉字更细粒度的汉字部件为基本单元来处理汉字,为中文信息处理提供了一种新思路。本文介绍了在开放式软件中,实现中文信息按汉字部件查找的设计方法。     

Machine Learning for Intelligent Processing of Printed Documents

A paper document processing system is an information system component which transforms information on printed or handwritten documents into a computer-revisable form. In intelligent systems for paper document processing this information capture process is based on knowledge of the specific layout and logical structures of the documents. This article proposes the application of machine learning techniques to acquire the specific knowledge required by an intelligent document processing system, named WISDOM++, that manages printed documents, such as letters and journals. Knowledge is represented by means of decision trees and first-order rules automatically generated from a set of training documents. In particular, an incremental decision tree learning system is applied for the acquisition of decision trees used for the classification of segmented blocks, while a first-order learning system is applied for the induction of rules used for the layout-based classification and understanding of documents. Issues concerning the incremental induction of decision trees and the handling of both numeric and symbolic data in first-order rule learning are discussed, and the validity of the proposed solutions is empirically evaluated by processing a set of real printed documents.     

Performance evaluation and comparison of parallel conjugate gradient on modern multi-core accelerator and massively parallel systems

Two parallel computer paradigms available today are multi-core accelerators such as the Sony, Toshiba and IBM Cell or Graphics Processing Unit (GPUs), and massively parallel message-passing machines such as the IBM Blue Gene (BG). The solution of systems of linear equations is one of the most central processing unit-intensive steps in engineering and simulation applications and can greatly benefit from the multitude of processing cores and vectorisation on today's parallel computers. We parallelise the conjugate gradient (CG) linear equation solver on the Cell Broadband Engine and the IBM Blue Gene/L machine. We perform a scalability analysis of CG on both machines across 1, 8 and 16 synergistic processing elements and 1–32 cores on BG with heptadiagonal matrices. The results indicate that the multi-core Cell system outperforms by three to four times the massively parallel BG system due to the Cell's higher communication bandwidth and accelerated vector processing capability.     

An architecture of an intelligent system for routing user requests in a network of heterogeneous databases

We present a general purpose model for routing user requests, e.g. queries, in a network of autonomous heterogeneous databases. The database schemas and other information on the database nodes are used to construct a multi-level knowledge-base (MKB) that resides in various nodes. Access to the databases is not done by creating direct connections between the user and the nodes where the data are presumably located. Rather, the user approaches the network by contents via an intelligent system that utilizes the MKB in order to identify the nodes and databases where the most relevant information resides, and establishes access routes to those nodes.     

Combining automatic acquisition of knowledge with machine learning approaches for multilingual temporal recognition and normalization

This paper presents an improvement in the temporal expression (TE) recognition phase of a knowledge based system at a multilingual level. For this purpose, the combination of different approaches applied to the recognition of temporal expressions are studied. In this work, for the recognition task, a knowledge based system that recognizes temporal expressions and had been automatically extended to other languages (TERSEO system) was combined with a system that recognizes temporal expressions using machine learning techniques. In particular, two different techniques were applied: maximum entropy model (ME) and hidden Markov model (HMM), using two different types of tagging of the training corpus: (1) BIO model tagging of literal temporal expressions and (2) BIO model tagging of simple patterns of temporal expressions. Each system was first evaluated independently and then combined in order to: (a) analyze if the combination gives better results without increasing the number of erroneous expressions in the same percentage and (b) decide which machine learning approach performs this task better. When the TERSEO system is combined with the maximum entropy approach the best results for F-measure (89%) are obtained, improving TERSEO recognition by 4.5 points and ME recognition by 7.     

Construction and analysis of ground models and their refinements as a foundation for validating computer-based systems

We explain why for the verified software challenge proposed in Hoare (J ACM 50(1): 63–69, 2003), Hoare and Misra (Verified software: theories, tools, experiments. Vision of a Grand Challenge project. In: [Meyer05]) to gain practical impact, one needs to include rigorous definitions and analysis, prior to code development and comprising both experimental validation and mathematical verification, of ground models, i.e., blueprints that describe the required application-content of programs. This implies the need to link via successive refinements the relevant properties of such high-level models in a traceable and checkable way to code a compiler can verify. We outline the Abstract State Machines (ASM) method, a discipline for reliable system development which allows one to bridge the gap between informal requirements and executable code by combining application-centric experimentally validatable system modelling with mathematically verifiable stepwise detailing of abstract models to compile-time-verifiable code.     

CLPL: Providing software infrastructure for the systematic and effective construction of complex collaborative learning systems

Over the last decade, e-Learning and in particular Computer-Supported Collaborative Learning (CSCL) needs have been evolving accordingly with more and more demanding pedagogical and technological requirements. As a result, high customization and flexibility are a must in this context, meaning that collaborative learning practices need to be continuously adapted, adjusted, and personalized to each specific target learning group. These very demanding needs of the CSCL domain represent a great challenge for the research community on software development to satisfy.This contribution presents and evaluates a previous research effort in the form of a generic software infrastructure called Collaborative Learning Purpose Library (CLPL) with the aim of meeting the current and demanding needs found in the CSCL domain. To this end, we experiment with the CLPL in order to offer an advanced reuse-based service-oriented software engineering methodology for developing CSCL applications in an effective and timely fashion. A validation process is provided by reporting on the use of the CLPL platform as the primary resource for the Master's thesis courses at the Open University of Catalonia when developing complex software applications in the CSCL domain.The ultimate aim of the whole research is to yield effective CSCL software systems capable of supporting and enhancing the current on-line collaborative learning practices.