LoGID: An adaptive framework combining local and global incremental learning for dynamic selection of ensembles of HMMs

Pattern Recognition

Volumn 45, Issue 9, 2012, Pages 3544-3556

  Cavalin, Paulo R ^a   Sabourin, Robert ^b   Suen, Ching Y ^c  

^a UNIVERSIDADE FEDERAL DO TOCANTINS   (Brazil)

^b ÉCOLE DE TECHNOLOGIE SUPÉRIEURE   (Canada)

^c Gina Cody School of Engineering and Computer Science   (Canada)

Author keywords

Adaptive systems; Dynamic selection; Ensembles of classifiers; Hidden Markov models; Incremental learning

Indexed keywords

ADAPTIVE FRAMEWORK; BASE CLASSIFIERS; BASELINE SYSTEMS; BATCH LEARNING; DYNAMIC SELECTION; ENSEMBLES OF CLASSIFIERS; INCREMENTAL LEARNING; LEARNING PHASIS; NEW MEMBERS; PERFORMANCE OF SYSTEMS; RECOGNITION RATES; SMALL TRAINING; STATE-OF-THE-ART APPROACH; TEST SAMPLES;

ADAPTIVE SYSTEMS; ALGORITHMS; DATA HANDLING; LAKES; PATTERN RECOGNITION SYSTEMS;

HIDDEN MARKOV MODELS;

EID: 84861630536     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2012.02.034     Document Type: Conference Paper

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