Practical conditions for effectiveness of the universum learning

IEEE Transactions on Neural Networks

Volumn 22, Issue 8, 2011, Pages 1241-1255

  Cherkassky, Vladimir ^a   Dhar, Sauptik ^a   Dai, Wuyang ^b  

^a University of Minnesota   (United States)

^b Boston University   (United States)

Author keywords

Learning through contradiction; model selection; support vector machines (SVMs); Universum SVM

Indexed keywords

APPLICATION DATA; DECISION BOUNDARY; EMPIRICAL COMPARISON; HIGH DIMENSIONAL DATA; INDUCTIVE LEARNING METHODS; INPUT FEATURES; LEARNING PROCESS; LEARNING SETTINGS; LEARNING THROUGH CONTRADICTION; MODEL SELECTION; NORMAL DIRECTION; NUMBER OF DATUM; PRIORI KNOWLEDGE; SUPPORT VECTOR; TRAINING SAMPLE; UNIVARIATE; UNIVERSUM SVM;

STANDARDS;

SUPPORT VECTOR MACHINES;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPARATIVE STUDY; METHODOLOGY; RANDOMIZATION; SUPPORT VECTOR MACHINE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; PATTERN RECOGNITION, AUTOMATED; RANDOM ALLOCATION; SUPPORT VECTOR MACHINES;

EID: 80051551588     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2011.2157522     Document Type: Article

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