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Multicategory psi-learning and support vector machine

Liu, Yufeng
http://rave.ohiolink.edu/etdc/view?acc_num=osu1085424065

Abstract Details

2004, Doctor of Philosophy, Ohio State University, Statistics.
Many margin-based classification techniques such as support vector machine (SVM) and psi-learning deliver high performance by directly focusing on estimating the decision boundary, as opposed to estimating the conditional probabilities via regression techniques. As a result, multicategory classification is often treated separately from binary classification; no straightforward generalization is possible. We propose a novel multicategory generalization particularly for psi-learning and with SVM as a by-product, without involving estimation of conditional probabilities, retaining their advantage in the binary case. A statistical learning theory for multicategory psi-learning is developed. To handle the nonconvex minimization problem of psi-learning, we propose computational tools for multicategory psi by utilizing differenced convex (d.c.) programming. We examine the operating characteristics of the proposed methodology via numerical examples, and we show that psi-learning outperforms SVM in terms of generalization.
Xiaotong Shen (Advisor)
71 p.
Statistics
classification; concave minimization; d.c. algorithms; generalization error; margins; nonconvex minimization; outer approximation; quadratic programming; support vectors

Recommended Citations

Citations

  • Liu, Y. (2004). Multicategory psi-learning and support vector machine [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1085424065

    APA Style (7th edition)

  • Liu, Yufeng. Multicategory psi-learning and support vector machine. 2004. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1085424065.

    MLA Style (8th edition)

  • Liu, Yufeng. "Multicategory psi-learning and support vector machine." Doctoral dissertation, Ohio State University, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=osu1085424065

    Chicago Manual of Style (17th edition)

osu1085424065
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© 2004, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.