期刊
NEURAL NETWORKS
卷 22, 期 5-6, 页码 544-557出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2009.06.042
关键词
Machine learning; SVM; SVM; Hidden information; Privileged information; Learning with teacher; Oracle SVM
资金
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [0916200] Funding Source: National Science Foundation
In the Afterward to the second edition of the book Estimation of Dependences Based on Empirical Data by V. Vapnik, an advanced learning paradigm called Learning Using Hidden Information (LUHI) was introduced. This Afterward also suggested an extension of the SVM method (the so called SVM gamma + method) to implement algorithms which address the LUHI paradigm (Vapnik, 1982-2006, Sections 2.4.2 and 2.5.3 of the Afterword). See also (Vapnik, Vashist, & Pavlovitch, 2008, 2009) for further development of the algorithms. In contrast to the existing machine learning paradigm where a teacher does not play an important role, the advanced learning paradigm considers some elements of human teaching. In the new paradigm along with examples, a teacher can provide Students with hidden information that exists in explanations, comments, comparisons, and so on. This paper discusses details of the new paradigm(1) and corresponding algorithms, introduces some new algorithms, considers several specific forms of privileged information, demonstrates superiority of the new learning paradigm over the classical learning paradigm when solving practical problems, and discusses general questions related to the new ideas. (C) 2009 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据