SVM viability controller active learning

Abstract : We use support vector machines (SVMs) to compute the actions which maintain a dynamical system within a defined subset of its state space. The principles of our method are inspired by the viability theory. We use SVMs to approximate the viability kernel which is the set of states from which it is possible to maintain the system. The actions to perform on the system can then be easily computed from the SVM, whatever the starting point. The major limitation of the approach is the exponentially growing number of training examples when the dimension of the state space increases. We use active learning to limit this number.
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Communication dans un congrès
Kernel machines and Reinforcement Learning Workshop - ICML 2006, 2006, United States
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https://hal.archives-ouvertes.fr/hal-00616861
Contributeur : Laetitia Chapel <>
Soumis le : mercredi 24 août 2011 - 16:45:23
Dernière modification le : mercredi 24 août 2011 - 16:45:23

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  • HAL Id : hal-00616861, version 1

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Laetitia Chapel, Guillaume Deffuant. SVM viability controller active learning. Kernel machines and Reinforcement Learning Workshop - ICML 2006, 2006, United States. <hal-00616861>

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