Efficient kernelized prototype based classification

International Journal of Neural Systems

Volumn 21, Issue 6, 2011, Pages 443-457

  Schleif, F M ^a   Villmann, Thomas ^b   Hammer, Barbara ^a   Schneider, Petra ^c  

^a BIELEFELD UNIVERSITY   (Germany)

^b UNIVERSITY OF APPLIED SCIENCES MITTWEIDA   (Germany)

^c UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

classification; interpretable models; kernel; Prototype learning; vector quantization

Indexed keywords

APPROXIMATION TECHNIQUES; DATA SETS; EFFECTIVE ALGORITHMS; EUCLIDEAN DISTANCE MEASURE; GENERALIZATION ERROR BOUNDS; GENERALIZED LEARNING VECTOR QUANTIZATION; KERNEL; LEARNING COMPLEXITY; MULTIPLE DOMAINS; PROTOTYPE LEARNING; PROTOTYPE-BASED CLASSIFIER; PUBLIC DATA; REAL WORLD DATA;

CLASSIFICATION (OF INFORMATION); ERROR ANALYSIS; LEARNING ALGORITHMS;

VECTOR QUANTIZATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER PROGRAM; COMPUTER SIMULATION; FACTUAL DATABASE; METHODOLOGY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DATABASES, FACTUAL; PATTERN RECOGNITION, AUTOMATED; SOFTWARE;

EID: 82655173326     PISSN: 01290657     EISSN: None     Source Type: Journal    
DOI: 10.1142/S012906571100295X     Document Type: Article

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