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What is Statistical Active Learning

Encyclopedia of Artificial Intelligence
The set of active learning algorithms in which the sample selection criteria is based on some statistical objective function, such as minimization of generalisation error, bias and variance. Statistical active learning is usually statistically optimal.
Published in Chapter:
Active Learning with SVM
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
DOI: 10.4018/978-1-59904-849-9.ch001
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.
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