Boosting active learning to optimality: A tractable Monte-Carlo, billiard-based algorithm

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5782 LNAI, Issue PART 2, 2009, Pages 302-317

  Rolet, Philippe ^a   Sebag, Michèle ^a   Teytaud, Olivier ^a  

^a CNRS   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE LEARNERS; ACTIVE LEARNING; APPLICATION DOMAINS; COMPUTATIONAL CONSTRAINTS; FINITE HORIZONS; GENERALIZATION ERROR; MONTE CARLO; MULTI ARMED BANDIT; OPTIMAL POLICIES; OPTIMALITY; PROOF OF PRINCIPLES; SAMPLING STRATEGIES; STAND -ALONE; TRAINING SETS;

APPROXIMATION ALGORITHMS; EDUCATION; OPTIMIZATION; REINFORCEMENT; ROBOT LEARNING;

LEARNING ALGORITHMS;

EID: 70349956665     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04174-7_20     Document Type: Conference Paper

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