Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31353
Title: Probabilistic large margin machine
Authors: Wang, D
Yeung, DS
Tsang, ECC
Keywords: Learning (artificial intelligence)
Optimisation
Pattern classification
Probability
Issue Date: 2006
Publisher: IEEE
Source: 2006 International Conference on Machine Learning and Cybernetics, 13-16 August 2006, Dalian, China, p. 2190-2195 How to cite?
Abstract: Large margin learning has been widely applied in solving supervised classification problems. One representative model in large margin learning is the support vector machine (SVM). As the linear classification constraints in the SVM optimization problem are determined with certainty, the performance of SVM is limited. In this study, we propose a new large margin learning model, named probabilistic large margin machine (PLMM), with the linear classification constraints bounded by probabilistic thresholds. In comparison with the SVM, the PLMM incorporates the prior probabilities and the distribution information of each class into the decision hyperplane learning. Mathematically the optimization problem involved in the PLMM can be treated as only one second order cone programming (SOCP) problem, which can he solved efficiently. The experimental results demonstrate the effectiveness of the PLMM model
URI: http://hdl.handle.net/10397/31353
ISBN: 1-4244-0061-9
DOI: 10.1109/ICMLC.2006.258618
Appears in Collections:Conference Paper

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