Prediction of maize single-cross hybrid performance: Support vector machine regression versus best linear prediction

Theoretical and Applied Genetics

Volumn 120, Issue 2, 2010, Pages 415-427

  Maenhout, Steven ^a   de Baets, Bernard ^b   Haesaert, Geert ^a  

^a UNIVERSITY COLLEGE GHENT   (Belgium)

^b GHENT UNIVERSITY   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURATE PREDICTION; BREEDING PROGRAMMES; CROSS VALIDATION; FIELD EVALUATION; FIELD EXPERIMENT; FIELD TRIAL; HYBRID PLANTS; INFORMATION CONTENTS; LINEAR MIXED MODEL ANALYSIS; LINEAR PREDICTION; LINEAR PREDICTION MODELS; MAIZE BREEDING; MAIZE HYBRIDS; MOLECULAR FINGERPRINT; PHENOTYPIC DATA; PREDICTION ACCURACY; PREDICTION METHODS; PREDICTION MODEL; PREDICTIVE CAPABILITIES; REPRESENTATIVE SAMPLE; SUPPORT VECTOR MACHINE REGRESSIONS; TRAINING EXAMPLE;

ECONOMIC ANALYSIS; IMAGE RETRIEVAL; MATHEMATICAL MODELS; REGRESSION ANALYSIS; ROUGH SET THEORY; SUPPORT VECTOR MACHINES;

COST REDUCTION;

ARTICLE; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MODEL; GENETIC MARKER; GENETICS; HETEROSIS; HYBRIDIZATION; MAIZE; PHENOTYPE; REGRESSION ANALYSIS; STATISTICAL MODEL;

ARTIFICIAL INTELLIGENCE; GENETIC MARKERS; HYBRID VIGOR; HYBRIDIZATION, GENETIC; LINEAR MODELS; MODELS, GENETIC; PHENOTYPE; REGRESSION ANALYSIS; ZEA MAYS;

ZEA MAYS;

EID: 75849127046     PISSN: 00405752     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00122-009-1200-5     Document Type: Article

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