GDTW-P-SVMs: Variable-length time series analysis using support vector machines

Neurocomputing

Volumn 99, Issue , 2013, Pages 270-282

  Jalalian, Arash ^a   Chalup, Stephan K ^a  

^a UNIVERSITY OF NEWCASTLE   (Australia)

Author keywords

Dynamic Time Warping; Sequential data analysis; Support vector machines; Time series classification

Indexed keywords

CLASSIFICATION METHODS; DATA SETS; DISTANCE MEASURE; DYNAMIC TIME WARPING; DYNAMIC TIME WARPING ALGORITHMS; FEATURE VECTORS; GAUSSIANS; INPUT SERIES; KERNEL FUNCTION; KERNEL MATRICES; MAXIMUM MARGIN; POSITIVE DEFINITE; REAL WORLD DATA; SCALE-INVARIANT; SEQUENTIAL DATA ANALYSIS; TEST DATA; TIME SERIES CLASSIFICATIONS; TRAJECTORY DATA; UCI MACHINE LEARNING REPOSITORY;

CLASSIFICATION (OF INFORMATION); CLUSTERING ALGORITHMS; TIME SERIES ANALYSIS; VIRTUAL REALITY;

SUPPORT VECTOR MACHINES;

ARTICLE; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; DATA ANALYSIS; GAUSSIAN DYNAMIC TIME WARPING; INTERMETHOD COMPARISON; KERNEL METHOD; MATHEMATICAL COMPUTING; MATHEMATICAL VARIABLE; PRIORITY JOURNAL; STATISTICAL CONCEPTS; SUPPORT VECTOR MACHINE; TIME SERIES ANALYSIS;

EID: 84867862095     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.07.006     Document Type: Article

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