Support Vector Machines: Induction Principle, Adaptive Tuning and Prior 
Knowledge

Chapelle, O

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Support Vector Machines: Induction Principle, Adaptive Tuning and Prior Knowledge

Chapelle, O. (2004). Support Vector Machines: Induction Principle, Adaptive Tuning and Prior Knowledge. PhD Thesis, Universit ́e Pierre et Marie Curie: Paris VI, Paris, France.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-E13F-6 Version Permalink: https://hdl.handle.net/21.11116/0000-0005-9E34-F

Genre: Thesis

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http://olivier.chapelle.cc/pub/thesis.pdf (Any fulltext) Open Access status unknown

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Creators:
Chapelle, O^{1, 2}, Author

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1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794

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Abstract: This thesis presents a theoretical and practical study of Support
Vector Machines (SVM) and related learning algorithms. In a first part,
we introduce a new induction principle from which SVMs can be derived, but
some new algorithms are also presented in this framework.
In a second part, after studying how to estimate the generalization
error of an SVM, we suggest to choose the kernel parameters of an SVM
by minimizing this estimate. Several applications such as feature
selection are presented. Finally the third part deals with the incoporation
of prior knowledge in a learning algorithm and more specifically, we
studied the case of known invariant transormations and the use
of unlabeled data.

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Dates: Published Online: 2002-06Date issued: 2004-04

Publication Status: Issued

Pages: 180

Publishing info: Paris, France : Universit ́e Pierre et Marie Curie: Paris VI

Table of Contents: -

Rev. Type: -

Identifiers: BibTex Citekey: 2167

Degree: PhD

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