The generalization error bound of support vector machine (SVM) depends on the ratio of radius and margin, while standard SVM only considers the maximization of the margin but ignores the minimization of the radius. Several approaches have been proposed to integrate radius and margin for joint learning of feature transformation and SVM classifier. However, most of them either require the form of...

In this paper, the cooperation of four feature families for handwritten digit recognition using SVM (Support Vector Machine) classifiers is examined. We investigate the advantages and weaknesses of various cooperation schemes based on classifier decision fusion using statistical reasoning. Although most of presented cooperation schemes are variations and adaptations of existing ones, such an ex...

current inversion condition leads to incorrect operation of current based directional relay in power system with series compensated device. application of the intelligent system for fault direction classification has been suggested in this paper. a new current directional protection scheme based on intelligent classifier is proposed for the series compensated line. the proposed classifier uses ...

In named entity recognition (NER) for biomedical literature, approaches based on combined classifiers have demonstrated great performance improvement compared to a single (best) classifier. This is mainly owed to sufficient level of diversity exhibited among classifiers, which is a selective property of classifier set. Given a large number of classifiers, how to select different classifiers to ...

Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process...

This paper describes the pattern recognition technique based on multiscale discrete wavelet transform(MDWT) and least square support vector machine (LS-SVM) for the classification of EEG signals. The different statistical features are extracted from each EEG signal corresponding to various seizer and nonsiezer brain functions, using MDWT. Further these sets of features are fed to the LS-SVM mul...

In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the comput...

to prospect mineral deposits at regional scale, recognition and classification of hydrothermal alteration zones using remote sensing data is a popular strategy. due to the large number of spectral bands, classification of the hyperspectral data may be negatively affected by the hughes phenomenon. a practical way to handle the hughes problem is preparing a lot of training samples until the size ...

Proximal SVM (PSVM), which is a variation of standard SVM, leads to an extremely faster and simpler algorithm for generating a linear or nonlinear classifier than classical SVM. An efficient incremental method for linear PSVM classifier has been introduced, but it can’t apply to nonlinear PSVM and incremental technique is the base of online learning and large data set training. In this paper we...