Sparse kernel-based ensemble learning with fully optimized kernel parameters for hyperspectral classification problems

IEEE Transactions on Geoscience and Remote Sensing

Volumn 51, Issue 2, 2013, Pages 787-802

  Gurram, Prudhvi ^a   Kwon, Heesung ^a  

^a U S ARMY RESEARCH LABORATORY   (United States)

Author keywords

Chemical plume detection; ensemble learning; kernel parameter optimization; sparse kernel learning; support vector machine (SVM)

Indexed keywords

AGGREGATION PROCESS; BACKGROUND CLUTTER; BANDWIDTH PARAMETERS; BINARY CLASSIFICATION; CHEMICAL PLUMES; CORRESPONDING WEIGHTS; CROSS VALIDATION; DATA SETS; ENSEMBLE LEARNING; ENSEMBLE LEARNING ALGORITHM; ENSEMBLE TECHNIQUES; GAUSSIAN KERNELS; HYPER-SPECTRAL CLASSIFICATION; HYPERSPECTRAL; HYPERSPECTRAL DATA; INDIVIDUAL CLASSIFIERS; INPUT DATAS; KERNEL MATRICES; KERNEL PARAMETER; KERNEL PARAMETER OPTIMIZATION; L1 NORM; RADIUS-MARGIN BOUND; ROBUST CLASSIFICATION; SEPARATING HYPERPLANE; SPARSE KERNELS; SPECTRAL FEATURE; SVM CLASSIFIERS; WEAK CLASSIFIERS; WEIGHTING COEFFICIENT;

AGGLOMERATION; BANDWIDTH; CHEMICAL DETECTION; LEARNING ALGORITHMS; SUPPORT VECTOR MACHINES;

OPTIMIZATION;

ALGORITHM; DATA SET; DETECTION METHOD; ENSEMBLE FORECASTING; GAUSSIAN METHOD; IMAGE CLASSIFICATION; OPTIMIZATION; PARAMETERIZATION; PLUME; REMOTE SENSING;

EID: 84872920883     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2012.2203603     Document Type: Article

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