Active Learning with SVM

View Sample PDF

Author(s): Jun Jiang (City University of Hong Kong, Hong Kong)and Horace H.S. Ip (City University of Hong Kong, Hong Kong)
Copyright: 2009
Pages: 7
Source title: Encyclopedia of Artificial Intelligence
Source Author(s)/Editor(s): Juan Ramón Rabuñal Dopico (University of A Coruña, Spain), Julian Dorado (University of A Coruña, Spain)and Alejandro Pazos (University of A Coruña, Spain)
DOI: 10.4018/978-1-59904-849-9.ch001

Keywords: Artificial Intelligence / Computer Science & IT / Information Science Reference

Purchase

View Active Learning with SVM on the publisher's website for pricing and purchasing information.

Abstract

Jiang, Jun; IP Horace H. S. With the increasing demand of multimedia information retrieval, such as image and video retrieval from the Web, there is a need to find ways to train a classifier when the training dataset is combined with a small number of labelled data and a large number of unlabeled one. Traditional supervised or unsupervised learning methods are not suited to solving such problems particularly when the problem is associated with data in a high-dimension space. In recent years, many methods have been proposed that can be broadly divided into two groups: semi-supervised and active learning (AL). Support Vector Machine (SVM) has been recognized as an efficient tool to deal with high-dimensionality problems, a number of researchers have proposed algorithms of Active Learning with SVM (ALSVM) since the turn of the Century. Considering their rapid development, we review, in this chapter, the state-of-the-art of ALSVM for solving classification problems.

The IRMA Community

Research IRM

Active Learning with SVM

Purchase

Abstract

Related Content

IRMA Sponsors