A novel LS-SVMs hyper-parameter selection based on particle swarm optimization

Neurocomputing

Volumn 71, Issue 16-18, 2008, Pages 3211-3215

  Guo, X C ^a,b   Yang, J H ^a   Wu, C G ^a,c   Wang, C Y ^a   Liang, Y C ^a  

^a JILIN UNIVERSITY   (China)

^b NORTHEAST ELECTRIC POWER UNIVERSITY   (China)

^c INSTITUTE OF AUTOMATION   (China)

Author keywords

Classification; Least squares support vector machines; Parameter selection; Particle swarm optimization

Indexed keywords

COMPUTATIONAL FLUID DYNAMICS; IMAGE RETRIEVAL; LEARNING SYSTEMS; NEURAL NETWORKS; OPTIMIZATION; PROBABILITY DENSITY FUNCTION; SUPPORT VECTOR MACHINES; VECTORS;

ANALYTIC PROPERTIES; BENCHMARK DATUMS; CLASSIFICATION; GENERALIZATION PERFORMANCES; HYPER PARAMETERS; KERNEL FAMILIES; KERNEL FUNCTIONS; LEAST-SQUARES SUPPORT VECTOR MACHINES; PARAMETER SELECTION; PARTICLE SWARM OPTIMIZATION; PARTICLE SWARMS; PRIORI KNOWLEDGES; RADIAL BASES; RBF KERNEL FUNCTIONS; SUPPORT VECTORS; TEST PERFORMANCES;

PARTICLE SWARM OPTIMIZATION (PSO);

CLASSIFICATION ALGORITHM; CLASSIFIER; CONFERENCE PAPER; FEASIBILITY STUDY; INFORMATION PROCESSING; KERNEL METHOD; PRIORITY JOURNAL; QUALITY CONTROL; RADIAL BASED FUNCTION; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINE;

EID: 56549111881     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2008.04.027     Document Type: Conference Paper

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