NDLI: An ellipsoid constrained quadratic programming (ECQP) approach to MCE training of MQDF-based classifiers for handwriting recognition

Content Provider	IEEE Xplore Digital Library
Author	Yongqiang Wang Peng Liu Qiang Huo
Copyright Year	2008
Description	Author affiliation: Microsoft Res. Asia, Beijing (Yongqiang Wang; Peng Liu; Qiang Huo)
Abstract	In this study, we propose a novel optimization algorithm for minimum classification error (MCE) training of modified quadratic discriminant function (MQDF) models. An ellipsoid constrained quadratic programming (ECQP) problem is formulated with an efficient line search solution derived, and a subspace combination condition is proposed to simplify the problem in certain cases. We show that under the perspective of constrained optimization, the MCE training of MQDF models can be solved by ECQP with some reasonable approximation, and the hurdle of incomplete covariances can be handled by subspace combination. Experimental results on the Nakayosi/Kuchibue online handwritten Kanji character recognition task show that compared with the conventional generalized probabilistic descent (GPD) algorithm, the new approach achieves about 7% relative error rate reduction.
Starting Page	1
Ending Page	4
File Size	160261
Page Count	4
File Format	PDF
ISBN	9781424421749
ISSN	10514651
DOI	10.1109/ICPR.2008.4761829
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Ellipsoids Quadratic programming Handwriting recognition Constraint optimization Subspace constraints Character recognition Asia Classification algorithms Error analysis Constraint theory
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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