Leveraging k-NN for generic classification boosting

Neurocomputing

Volumn 80, Issue , 2012, Pages 3-9

  Piro, Paolo ^a,d   Nock, Richard ^b   Nielsen, Frank ^c,e   Barlaud, Michel ^a  

^a UNIVERSITÉ CÔTE D'AZUR   (France)

^b UNIVERSITÉ DES ANTILLES ET DE LA GUYANE   (France)

^c ÉCOLE POLYTECHNIQUE   (France)

^d ISTITUTO ITALIANO DI TECNOLOGIA   (Italy)

^e SONY COMPUTER SCIENCE LABORATORIES INC   (Japan)

Author keywords

Boosting; K NN classification; Surrogate risks

Indexed keywords

BAYES ERROR; BOOSTING; BOOSTING APPROACH; CLASSIFICATION ERRORS; DATA SETS; EFFECTIVE TOOL; FILTERING STRATEGIES; GENERALIZATION PROPERTIES; HIGH-DIMENSIONAL; K-NEAREST NEIGHBORS; LOSS FUNCTIONS; MACHINE LEARNING TECHNIQUES; MISCLASSIFICATION RATES; NEAREST NEIGHBORS; STATE OF THE ART; TRAINING DATA; VOTING RULES;

ADAPTIVE BOOSTING;

CLASSIFICATION (OF INFORMATION);

ARTICLE; BAYESIAN LEARNING; CLASSIFICATION ALGORITHM; ERROR; INFORMATION PROCESSING; K NEAREST NEIGHBOR; LEARNING ALGORITHM; MATHEMATICAL ANALYSIS; NOISE REDUCTION; PRIORITY JOURNAL; UNIVERSAL NEAREST NEIGHBOR;

EID: 84855222926     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.07.026     Document Type: Article

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