A method based on Electrical Capacitance Tomography (ECT) and an improved Least Squares Support Vector Machine (LS-SVM) is proposed for void fraction measurement of oil-gas two-phase flow. In the modeling stage, to solve the two problems in LS-SVM, pruning skills are employed to make LS-SVM sparse and robust; then the Real-Coded Genetic Algorithm is introduced to solve the difficult problem...

Standard classification algorithms aim to minimize the probability of making an incorrect classification. In many important applications, however, some kinds of errors are more important than others. In this report we review cost-sensitive extensions of standard support vector machines (SVMs). In particular, we describe cost-sensitive extensions of the C-SVM and the ν-SVM, which we denote the 2...

Since their introduction, ranking SVM models [11] have become a powerful tool for training content-based retrieval systems. All we need for training a model are retrieval examples in the form of triplet constraints, i.e. examples specifying that relative to some query, a database item a should be ranked higher than database item b. These types of constraints could be obtained from feedback of u...

In recent years, the algorithms of learning to rank have been proposed by researchers. However, in information retrieval, instances of ranks are imbalanced. After the instances of ranks are composed to pairs, the pairs of ranks are imbalanced too. In this paper, a cost-sensitive risk minimum model of pairwise learning to rank imbalanced data sets is proposed. Following this model, the algorithm...

Most algorithms of support vector machines (SVMs) operate in a batch mode. However, when the samples arrive sequentially, batch implementations of SVMs are computationally demanding due to the fact that they must be retrained from scratch. This paper proposes an incremental SVM algorithm that is suitable for the problems of sequentially arriving samples. Unlike previous SVM techniques, this new...

Support Vector Machines(SVMs) have been extensively researched in the data mining and machine learning communities for the last decade and actively applied to applications in various domains. SVMs are typically used for learning classification, regression, or ranking functions, for which they are called classifying SVM, support vector regression (SVR), or ranking SVM (or RankSVM) respectively. ...

The information on the World Wide Web is growing without bound. Users may have very diversified preferences in the pages they target through a search engine. It is therefore a challenging task to adapt a search engine to suit the needs of a particular community of users who share similar interests. In this paper, we propose a new algorithm, Ranking SVM in a Co-training Framework (RSCF). Essenti...