prediction the river flow discharge values are important in the surface water resources management. find an appropriate model to accurately predictionof this parameter is one of the most important ways to simulation and prediction. in this study three anfis, svm and gp models were evaluated and compared to modeling the monthly flow discharge of nazloochi river in tapik hydrometric station that ...

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...