A small sphere and large margin approach for novelty detection using training data with outliers

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 31, Issue 11, 2009, Pages 2088-2092

  Wu, Mingrui ^a   Ye, Jieping ^b  

^a YAHOO INC   (United States)

^b Arizona State University   (United States)

Author keywords

Kernel methods; Novelty detection; One class classification; Support vector machine

Indexed keywords

BASIC IDEA; CONVEX OPTIMIZATION PROBLEMS; KERNEL METHODS; NOVELTY DETECTION; ONE-CLASS CLASSIFICATION; SMALL SPHERES; TRAINING DATA;

ALGORITHMS; CONVEX OPTIMIZATION; GEARS; IMAGE RETRIEVAL; MULTILAYER NEURAL NETWORKS; SPHERES;

SUPPORT VECTOR MACHINES;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER SIMULATION; DECISION SUPPORT SYSTEM; METHODOLOGY; THEORETICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DECISION SUPPORT TECHNIQUES; MODELS, THEORETICAL; PATTERN RECOGNITION, AUTOMATED;

EID: 70349915779     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2009.24     Document Type: Article

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