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キーワード:
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要旨:
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.
