An efficient method to estimate labelled sample size for transductive LDA(QDA/MDA) based on bayes risk

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3201, Issue , 2004, Pages 274-285

  Liu, Han ^a   Yuan, Xiaobin ^a   Tang, Qianying ^a   Kustra, Rafal ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

DATA REDUCTION; GRAPH THEORY; PROBABILITY; PROBABILITY DENSITY FUNCTION; PROBLEM SOLVING; ARTIFICIAL INTELLIGENCE; BIOINFORMATICS; CLASSIFICATION (OF INFORMATION); DATA MINING; GAUSSIAN DISTRIBUTION; LEARNING ALGORITHMS; LEARNING SYSTEMS; RISK PERCEPTION;

BAYES RISK; DATASETS; GAUSSIAN DISTRIBUTION; LINEAR DISCRIMINANT CLASSIFIERS (LDA); ESTIMATION METHODS; LINEAR DISCRIMINANT CLASSIFIER; MULTIPLE CLASS; RELATIVE VALUE; SAMPLE SIZES; SEMI-SUPERVISED; SEMI-SUPERVISED CLASSIFICATION; SEMI-SUPERVISED LEARNING METHODS;

LEARNING SYSTEMS; SUPERVISED LEARNING;

EID: 22944435531     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-30115-8_27     Document Type: Conference Paper

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