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Título:
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Learning probability distributions generated by finite-state machines
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Autor/a:
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Castro Rabal, Jorge; Gavaldà Mestre, Ricard
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Otros autores:
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Universitat Politècnica de Catalunya. Departament de Ciències de la Computació; Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge |
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Abstract:
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We review methods for inference of probability distributions generated by probabilistic automata and related models for sequence generation. We focus on methods that can be proved to learn in the inference
in the limit and PAC formal models. The methods we review are state merging and state splitting methods for probabilistic deterministic automata and the recently developed spectral method for nondeterministic probabilistic automata. In both cases, we derive them from a high-level algorithm described in terms of the Hankel matrix of the distribution to be learned, given as an oracle, and then describe how to adapt that algorithm to account for the error introduced by a finite sample. |
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Abstract:
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Peer Reviewed |
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Materia(s):
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-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
-Machine learning
-Machine theory
-Probabilistic automata
-PAC formal models
-Finite-state machines
-Aprenentatge automàtic
-Màquines, Teoria de |
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Derechos:
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Tipo de documento:
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Artículo - Versión presentada
Capítulo o parte de libro |
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Editor:
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Springer
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Compartir:
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