Nonlinear logistic discrimination via regularized radial basis functions for classifying high-dimensional data

Annals of the Institute of Statistical Mathematics

Volumn 61, Issue 2, 2009, Pages 331-353

  Ando, Tomohiro ^a   Konishi, Sadanori ^b  

^a KEIO UNIVERSITY   (Japan)

^b KYUSHU UNIVERSITY   (Japan)

Author keywords

Bayes approach; Information criteria; Maximum penalized likelihood method; Radial basis functions

Indexed keywords

APPLIED STATISTICS; BASIS FUNCTIONS; BAYES APPROACH; BAYESIAN; CLASS LABELS; COMPLEX STRUCTURES; CONSTRUCTION PROCESS; COVARIATES; EXTENDED VERSIONS; FUTURE OBSERVATIONS; HIGH-DIMENSIONAL DATUM; HYBRID LEARNING; HYPER PARAMETERS; INFORMATION CRITERIA; LINEAR PREDICTORS; MAXIMUM PENALIZED LIKELIHOOD METHOD; MODEL EVALUATIONS; MONTE CARLO EXPERIMENTS; NON-PARAMETRIC METHODS; PARAMETER ESTIMATES; PENALIZED MAXIMUM-LIKELIHOOD ESTIMATIONS; PREDICTION PERFORMANCE; RADIAL BASIS FUNCTIONS; REAL DATA ANALYSIS; SIMULATION RESULTS; VARIABLE SELECTION PROBLEMS;

EDUCATION; INFORMATION THEORY; RADIAL BASIS FUNCTION NETWORKS;

MAXIMUM LIKELIHOOD ESTIMATION;

EID: 67349126855     PISSN: 00203157     EISSN: 15729052     Source Type: Journal    
DOI: 10.1007/s10463-007-0143-3     Document Type: Article

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