PAC learning of Probabilistic Automaton based on the Method of Moments

Glaude, Hadrien; Pietquin, Olivier

Document type :

Communication dans un congrès avec actes

Title :

PAC learning of Probabilistic Automaton based on the Method of Moments

Author(s) :

Glaude, Hadrien [Auteur]
Université de Lille, Sciences et Technologies
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Sequential Learning [SEQUEL]
Thales Airborne Systems
Pietquin, Olivier [Auteur]
Institut Universitaire de France [IUF]
Université de Lille, Sciences et Technologies
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Sequential Learning [SEQUEL]

Conference title :

International Conference on Machine Learning (ICML 2016)

City :

New York

Country :

Etats-Unis d'Amérique

Start date of the conference :

2016-06-19

HAL domain(s) :

Informatique [cs]/Apprentissage [cs.LG]

English abstract : [en]

Probabilitic Finite Automata (PFA) are gener-ative graphical models that define distributions with latent variables over finite sequences of symbols, a.k.a. stochastic languages. Traditionally , unsupervised learning of ...
Show more >Probabilitic Finite Automata (PFA) are gener-ative graphical models that define distributions with latent variables over finite sequences of symbols, a.k.a. stochastic languages. Traditionally , unsupervised learning of PFA is performed through algorithms that iteratively improves the likelihood like the Expectation-Maximization (EM) algorithm. Recently, learning algorithms based on the so-called Method of Moments (MoM) have been proposed as a much faster alternative that comes with PAC-style guarantees. However, these algorithms do not ensure the learnt automata to model a proper distribution , limiting their applicability and preventing them to serve as an initialization to iterative algorithms. In this paper, we propose a new MoM-based algorithm with PAC-style guarantees that learns automata defining proper distributions. We assess its performances on synthetic problems from the PAutomaC challenge and real datasets extracted from Wikipedia against previous MoM-based algorithms and EM algorithm.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

Files

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Open access
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https://hal.inria.fr/hal-01406889/document
Open access
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