Exploiting label dependencies for improved sample complexity

Machine Learning

Volumn 91, Issue 1, 2013, Pages 1-42

  Chekina, Lena ^a   Gutfreund, Dan ^b   Kontorovich, Aryeh ^a   Rokach, Lior ^a   Shapira, Bracha ^a  

^a BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

^b IBM RESEARCH   (United States)

Author keywords

Artificial datasets; Conditional and unconditional label dependence; Empirical experiment; Ensemble learning algorithms; Ensemble models diversity; Generalization bounds; Multi label classification; Multi label evaluation measures

Indexed keywords

ARTIFICIAL DATASETS; CONDITIONAL AND UNCONDITIONAL LABEL DEPENDENCE; EMPIRICAL EXPERIMENTS; ENSEMBLE LEARNING ALGORITHM; ENSEMBLE MODELS; EVALUATION MEASURES; GENERALIZATION BOUND; MULTI-LABEL CLASSIFICATIONS;

BENCHMARKING; DATA PROCESSING; LEARNING ALGORITHMS;

CLASSIFICATION (OF INFORMATION);

EID: 84875422639     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-012-5312-9     Document Type: Article

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