Sparse quasi-Newton optimization for semi-supervised support vector machines

ICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods

Volumn 1, Issue , 2012, Pages 45-54

  Gieseke, Fabian ^a   Airola, Antti ^b   Pahikkala, Tapio ^b   Kramer, Oliver ^a  

^a UNIVERSITY OF OLDENBURG   (Germany)

^b UNIVERSITY OF TURKU   (Finland)

Author keywords

Non convex optimization; Nystr m approximation; Quasi Newton methods; Semi supervised support vector machines; Sparse data

Indexed keywords

BINARY CLASSIFICATION APPROACH; BLACK-BOX OPTIMIZATION; CLASSIFICATION PERFORMANCE; LABELED DATA; NONCONVEX OPTIMIZATION; OPTIMIZATION SCHEME; OPTIMIZATION TASK; QUASI-NEWTON; QUASI-NEWTON METHODS; QUASI-NEWTON OPTIMIZATION; REAL WORLD DATA; REAL-WORLD SCENARIO; RESEARCH DIRECTIONS; RUN-TIME RESULTS; SEMI-SUPERVISED; SPARSE DATA; STATE-OF-THE-ART METHODS; UNLABELED DATA; YIELD MODELS;

CONVEX OPTIMIZATION; NEWTON-RAPHSON METHOD; PATTERN RECOGNITION; SUPPORT VECTOR MACHINES; VIRTUAL REALITY;

OPTIMIZATION;

EID: 84862194001     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

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