Kernels for generalized multiple-instance learning

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 30, Issue 12, 2008, Pages 2084-2097

  Tao, Qingping ^a   Scott, Stephen D ^b   Vinodchandran, N V ^b   Osugi, Thomas Takeo ^c   Mueller, Brandon ^d  

^a GC Image LLC   (United States)

^b UNIVERSITY OF NEBRASKA LINCOLN   (United States)

^c Sphere Communications   (United States)

^d Gallup Inc   (United States)

Author keywords

Biological sequence analysis; Content based image retrieval; Fully polynomial randomized approximation schemes; Generalized multiple instance learning; Kernels; Support vector machines

Indexed keywords

APPROXIMATION THEORY; CONCENTRATION (PROCESS); CONTENT BASED RETRIEVAL; GEARS; IMAGE RETRIEVAL; INFORMATION RETRIEVAL; LEARNING SYSTEMS; MULTILAYER NEURAL NETWORKS; SUPPORT VECTOR MACHINES; VECTORS;

BIOLOGICAL SEQUENCE ANALYSIS; CONTENT-BASED IMAGE RETRIEVAL; FULLY POLYNOMIAL RANDOMIZED APPROXIMATION SCHEMES; GENERALIZED MULTIPLE-INSTANCE LEARNING; KERNELS;

POLYNOMIAL APPROXIMATION;

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

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

EID: 56549100329     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2007.70846     Document Type: Article

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