SoftDoubleMaxMinOver: Perceptron-like training of support vector machines

IEEE Transactions on Neural Networks

Volumn 20, Issue 7, 2009, Pages 1061-1072

  Martinetz, Thomas ^a   Labusch, Kai ^a   Schneegaß, Daniel ^b  

^a UNIVERSITY OF LÜBECK   (Germany)

^b SIEMENS AG   (Germany)

Author keywords

Incremental learning; Maximum margin classification; Support vector machine (SVM)

Indexed keywords

COMPUTATIONAL EFFORT; FINITE NUMBER; INCREMENTAL LEARNING; ITERATIVE PROCEDURES; LEAST SQUARES SUPPORT VECTOR MACHINES; LINEARLY SEPARABLE; MARGIN CLASSIFIERS; MAXIMUM-MARGIN CLASSIFICATION; MINOVER ALGORITHM; PERCEPTRON; PERCEPTRON ALGORITHMS; SOFT MARGINS; SUPPORT VECTOR; SUPPORT VECTOR MACHINE (SVM); TRAINING PATTERNS; TWO-CLASS CLASSIFICATION PROBLEMS;

ALGORITHMS; GEARS; IMAGE RETRIEVAL; MULTILAYER NEURAL NETWORKS; VECTORS;

SUPPORT VECTOR MACHINES;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; COMPUTER PROGRAM; COMPUTER SIMULATION; EPIDEMIOLOGY; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIAS (EPIDEMIOLOGY); COMPUTER SIMULATION; LINEAR MODELS; NEURAL NETWORKS (COMPUTER); SOFTWARE; SOFTWARE VALIDATION;

EID: 67949123173     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2009.2016717     Document Type: Article

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