Global convergence of decomposition learning methods for support vector machines

IEEE Transactions on Neural Networks

Volumn 17, Issue 6, 2006, Pages 1362-1369

  Takahashi, Norikazu ^a   Nishi, Tetsuo ^a,b  

^a KYUSHU UNIVERSITY   (Japan)

^b WASEDA UNIVERSITY   (Japan)

Author keywords

Decomposition method; Global convergence; Quadratic programming (QP); Support vector machines (SVMs); Termination

Indexed keywords

COMPUTATIONAL METHODS; CONVERGENCE OF NUMERICAL METHODS; ITERATIVE METHODS; PROBLEM SOLVING; QUADRATIC PROGRAMMING;

DECOMPOSITION LEARNING METHODS; GLOBAL CONVERGENCE; QUADRATIC PROGRAMMING (QP) PROGRAMS; SUPPORT VECTOR MACHINES (SVMS);

LEARNING SYSTEMS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; INFORMATION RETRIEVAL; INFORMATION SCIENCE; METHODOLOGY; SIGNAL PROCESSING; SYSTEM ANALYSIS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; INFORMATION STORAGE AND RETRIEVAL; INFORMATION THEORY; PATTERN RECOGNITION, AUTOMATED; PROGRAMMING, LINEAR; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 34248677013     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2006.880584     Document Type: Article

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