Semi-supervised novelty detection using SVM entire solution path

IEEE Transactions on Geoscience and Remote Sensing

Volumn 51, Issue 4, 2013, Pages 1939-1950

  De Morsier, Frank ^a   Tuia, Devis ^a   Borgeaud, Maurice ^b   Gass, Volker ^a   Thiran, Jean Philippe ^a  

^a EPFL   (Switzerland)

^b ESA ESRIN   (Italy)

Author keywords

Change detection; cost sensitive support vector machine (CS SVM); learning from positive and unlabeled examples; low density (LD) separation; nested support vector machine (SVM); unsupervised parameter selection

Indexed keywords

CHANGE DETECTION; COST-SENSITIVE SUPPORT VECTOR MACHINES; LEARNING FROM POSITIVE AND UNLABELED EXAMPLES; LOW DENSITY; PARAMETER SELECTION;

ALGORITHMS; OPTIMIZATION; SEPARATION; SIGNAL DETECTION;

SUPPORT VECTOR MACHINES;

ALGORITHM; BOUNDARY; DATA SET; DENSITY; DISASTER MANAGEMENT; EXPERIMENTAL STUDY; FIRE; FLOOD; IMAGE CLASSIFICATION; INFORMATION MANAGEMENT; NUMERICAL MODEL; OPTIMIZATION; PARAMETERIZATION; PERFORMANCE ASSESSMENT; TEMPORAL ANALYSIS;

EID: 84875660598     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2012.2236683     Document Type: Article

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