DCSVM: fast multi-class classification using support vector machines

Support Vector Machines for Multi-class Classification

A b s t r a c t : Support vector machines (SVMs) are primarily designed for 2-class classification problems. Although in several papers it is mentioned that the combination of K SVMs can be used to solve a K-class classification problem, such a procedure requires some care. In this paper, the scaling problem of different SVMs is highlighted. Various normalization methods are proposed to cope wi...

Multi-class Support Vector Machines

Fast Training of Multi-Class Support Vector Machines

We present new decomposition algorithms for training multi-class support vector machines (SVMs), in particular the variants proposed by Lee, Lin, & Wahba (LLW) and Weston & Watkins (WW). Although these two types of machines have desirable theoretical properties, they have been rarely used in practice because efficient training algorithms have been missing. Training is accelerated by considering...

Multi-classification with Tri-class Support Vector Machines. A Review

In this article, with the aim to avoid the loss of information that occurs in the usual one-versus-one SVM decomposition procedure of the two-phases (decomposition, reconstruction) multi-classification scheme tri-class SVM approach is addressed. As the most relevant result, it will be demonstrated the robustness improvement of the proposed scheme based on tri-class machine versus that based on ...

Feature Selection for Multi-class Problems Using Support Vector Machines

Since feature selection can remove the irrelevant features and improve the performance of learning systems, it is an crucial step in machine learning. The feature selection methods using support vector machines have obtained satisfactory results, but the previous works are usually for binary classification, and needs auxiliary techniques to be extended to multiple classification. In this paper,...