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osu1124282485.pdf (369.28 KB)
ETD Abstract Container
Abstract Header
Generalization error rates for margin-based classifiers
Author Info
Park, Changyi
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=osu1124282485
Abstract Details
Year and Degree
2005, Doctor of Philosophy, Ohio State University, Statistics.
Abstract
Margin-based classifiers defined by functional margins are generally believed to yield high performance in classification. In this thesis, a general theory that quantifies the size of generalization error of a margin classifier is presented. The trade-off between geometric margins and training errors is captured, in addition to the complexity of a classification problem. The theory permits an investigation of the generalization ability of convex and nonconvex margin classifiers, including support vector machines (SVM), kernel logistic regression (KLR), and ψ-learning. Our theory indicates that the generalization ability of a certain class of nonconvex losses may be substantially faster than those for convex losses. Illustrative examples for both linear and nonlinear classification are provided.
Committee
Xiaotong Shen (Advisor)
Pages
63 p.
Subject Headings
Statistics
Keywords
Bayes risk
;
classification
;
consistency
;
convex
;
nonconvex
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Citations
Park, C. (2005).
Generalization error rates for margin-based classifiers
[Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1124282485
APA Style (7th edition)
Park, Changyi.
Generalization error rates for margin-based classifiers.
2005. Ohio State University, Doctoral dissertation.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=osu1124282485.
MLA Style (8th edition)
Park, Changyi. "Generalization error rates for margin-based classifiers." Doctoral dissertation, Ohio State University, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=osu1124282485
Chicago Manual of Style (17th edition)
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Document number:
osu1124282485
Download Count:
1,083
Copyright Info
© 2005, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.