Support-vector networks

A Comparative Approximate Economic Behavior Analysis Of Support Vector Machines And Neural Networks Models

Intelligent and Online Evaluation of Diabetes using Wireless Sensor Networks and Support Vector Machines Algorithm

Objective: International Diabetes Organization estimates that there are 285 million people worldwide who suffer from diabetes, and this figure is expected to increase to 450 million in next 20 years. According to statistics issued by the World Health Organization, diabetes is considered among ten leading causes of death in world and its prevalence in the population is growing.This paper deals w...

Support vector algorithms as regularization networks

Many recently proposed learning algorithms are clearly inspired by Suppport Vector Machines. Some of them were developed while trying to simplify the quadratic programming problem that has to be solved in training SVMs. Some others have been proposed to solve problems other than binary classification (for example one-class SVM for novelty detection). Though indeed attractive, for most of the le...

Regularization Networks and Support Vector Machines

Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples – in particular, the regression problem of approximating a multivariate function from sparse data. Radial Basis Functions, for example, are a special case of both regularization and Support Vector Machines. We review both formulations in the context of Vapnik’s theory of sta...

Support-Vector-Based Fuzzy Neural Networks

In this paper, novel fuzzy neural networks (FNNs) combining with support vector learning mechanism called support-vector-based fuzzy neural networks (SVFNNs) are proposed for pattern classification and function approximation. The SVFNNs combine the capability of minimizing the empirical risk (training error) and expected risk (testing error) of support vector learning in high dimensional data s...