Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability

Analytical Chemistry

Volumn 84, Issue 6, 2012, Pages 2686-2694

  Dingari, Narahara Chari ^a   Barman, Ishan ^a   Myakalwar, Ashwin Kumar ^b   Tewari, Surya P ^b   Kumar Gundawar, Manoj ^b  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b UNIVERSITY OF HYDERABAD   (India)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL SAMPLES; CHEMOMETRICES; CLASSIFICATION ACCURACY; CONCOMITANT REDUCTION; EXPERIMENTAL PARAMETERS; LINEAR TECHNIQUES; MISCLASSIFICATION RATES; NONLINEAR CLASSIFICATION; PARTIAL LEAST SQUARES; REAL-WORLD APPLICATION; ROBUST CLASSIFICATION; SAMPLE PREPARATION; SOFT INDEPENDENT MODELING OF CLASS ANALOGIES; SUPPORT VECTOR MACHINE (SVM); TRAINING SETS;

ATOMIC EMISSION SPECTROSCOPY; DIAGNOSIS; DISCRIMINANT ANALYSIS; PROCESS MONITORING; QUALITY ASSURANCE; SCREENING;

SUPPORT VECTOR MACHINES;

DRUG;

ARTICLE; CHEMISTRY; EVALUATION; LASER; METHODOLOGY; SENSITIVITY AND SPECIFICITY; SPECTROSCOPY; SUPPORT VECTOR MACHINE;

LASERS; PHARMACEUTICAL PREPARATIONS; SENSITIVITY AND SPECIFICITY; SPECTRUM ANALYSIS; SUPPORT VECTOR MACHINES;

EID: 84858719998     PISSN: 00032700     EISSN: None     Source Type: Journal    
DOI: 10.1021/ac202755e     Document Type: Article

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