Recently, the issue of machine condition monitoring and fault diagnosis as a part of
maintenance system became global due to the potential advantages to be gained from …

This is the first comprehensive introduction to Support Vector Machines (SVMs), a new
generation learning system based on recent advances in statistical learning theory. SVMs …

This article points out an important source of inefficiency in Platt's sequential minimal
optimization (SMO) algorithm that is caused by the use of a single threshold value. Using …

Since the introduction of support vector machines, we have witnessed the huge
development in theory, models, and applications of what is so-called kernel-based methods …

Text Classification, or the task of automatically assigning semantic categories to natural
language text, has become one of the key methods for organizing online information. Since …

This paper points out an important source of inefficiency in Smola and Scholkopf's (1998)
sequential minimal optimization (SMO) algorithm for support vector machine regression that …

Standard SVM training has O (m3) time and O (m2) space complexities, where m is the
training set size. It is thus computationally infeasible on very large data sets. By observing …

ABSTRACT Support Vector Machines (SVMs) and related kernel methods have become
increasingly popular tools for data mining tasks such as classification, regression, and …

Considerable efforts have been devoted to the implementation of an efficient optimization
method for solving the support vector machine dual problem. This chapter proposes an in …

The ν-support vector machine (ν-SVM) for classification proposed by Schölkopf, Smola,
Williamson, and Bartlett (2000) has the advantage of using a parameter ν on controlling the …