This paper presents a novel algorithm for density estimation which is based on the support vector machines (SVM) approach and it uses the Mean Field (MF) theory for developing an easy and efficient learning procedure for the SVM. The traditional formulation of the SVM density estimation decomposes the parameters of the problem into a quadratic optimization which can be solved using standard opt...

This paper presents a weighted support vector machine (WSVM) to improve the outlier sensitivity problem of standard support vector machine (SVM) for two-class data classification. The basic idea is to assign different weights to different data points such that the WSVM training algorithm learns the decision surface according to the relative importance of data points in the training data set. Th...

A new decomposition algorithm for training regression Support Vector Machines (SVM) is presented. The algorithm builds on the basic principles of decomposition proposed by Osuna et. al., and addresses the issue of optimal working set selection. The new criteria for testing optimality of a working set are derived. Based on these criteria, the principle of \maximal inconsistency" is proposed to f...

Support Vector Machines (SVM) is a practical algorithm that has been widely used in many areas. To guarantee its satisfying performance, it is important to set appropriate parameters of SVM algorithm. Sequential Minimal Optimization (SMO) is an effective training algorithm belonging to SVM, i.e.LS_SVM. Therefore, on the basis of the SMO algorithm and LS_SVM, which integrates SMO algorithm and L...

In this paper, we show that the popular C-SVM, soft-margin support vector classifier is equivalent to minimization of Buffered Probability of Exceedance (bPOE) by introducing a new SVM formulation, called the EC-SVM, which is derived as a bPOE minimization problem. Since it is derived from a simple bPOE minimization problem, the EC-SVM is simple to interpret with a meaningful free parameter, op...

An enhanced classification system for classification of brain tumor from MR images using association of kernels with support vector machine is developed and presented in this paper. Oriented Rician Noise Reduction Anisotropic Diffusion filter is used for image denoising. A modified fuzzy c-means algorithm termed as Penalized fuzzy c-means algorithm is used for image segmentation. The texture an...

As to classification problem, this paper puts forward the combinatorial optimization least squares support vector machine algorithm (COLS-SVM). Based on algorithmic analysis of COLS-SVM and improves on it, the improved COLS-SVM can be used on individual credit evaluation. As to regression problem, appropriate kernel function and parameters were selected based on the analysis of support vector r...

this paper is concerned with the development of a novel classifier for automatic mass detection of mammograms, based on contourlet feature extraction in conjunction with statistical and fuzzy classifiers. in this method, mammograms are segmented into regions of interest (roi) in order to extract features including geometrical and contourlet coefficients. the extracted features benefit from...

In view of the traditional support vector machine (SVM) learning algorithm’s learning parameters is not easy determined in the process of video key frame extraction and the accuracy is low, an independent perturbation variable difference SVM algorithm is used for video key frame extraction. First of all, analyzed the biological mechanism of differential evolution algorithm, put forward an impro...