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Received: 2017-01-04

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Crosschecked: 2018-05-10

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Ke Guo


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Frontiers of Information Technology & Electronic Engineering  2018 Vol.19 No.5 P.639-650


A new constrained maximum margin approach to discriminative learning of Bayesian classifiers

Author(s):  Ke Guo, Xia-bi Liu, Lun-hao Guo, Zong-jie Li, Zeng-min Geng

Affiliation(s):  Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing 100081, China; more

Corresponding email(s):   guoke@bit.edu.cn, liuxiabi@bit.edu.cn, guolunhao@bit.edu.cn, leezongjie@163.com, jsjgzm@bift.edu.cn

Key Words:  Discriminative learning, Statistical modeling, Bayesian pattern classifiers, Gaussian mixture models, UCI datasets

Ke Guo, Xia-bi Liu, Lun-hao Guo, Zong-jie Li, Zeng-min Geng. A new constrained maximum margin approach to discriminative learning of Bayesian classifiers[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(5): 639-650.

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publisher="Zhejiang University Press & Springer",

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%A Lun-hao Guo
%A Zong-jie Li
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700007

T1 - A new constrained maximum margin approach to discriminative learning of Bayesian classifiers
A1 - Ke Guo
A1 - Xia-bi Liu
A1 - Lun-hao Guo
A1 - Zong-jie Li
A1 - Zeng-min Geng
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
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SP - 639
EP - 650
%@ 2095-9184
Y1 - 2018
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1700007

We propose a novel discriminative learning approach for Bayesian pattern classification, called ‘constrained maximum margin (CMM)’. We define the margin between two classes as the difference between the minimum decision value for positive samples and the maximum decision value for negative samples. The learning problem is to maximize the margin under the constraint that each training pattern is classified correctly. This nonlinear programming problem is solved using the sequential unconstrained minimization technique. We applied the proposed CMM approach to learn Bayesian classifiers based on gaussian mixture models, and conducted the experiments on 10 UCI datasets. The performance of our approach was compared with those of the expectation-maximization algorithm, the support vector machine, and other state-of-the-art approaches. The experimental results demonstrated the effectiveness of our approach.




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