Support Vector Machine Classifier With Pinball Loss

Traditionally, the hinge loss is used to construct support vector machine (SVM) classifiers. The hinge loss is related to the shortest distance between sets and the corresponding classifier is hence sensitive to noise and unstable for re-sampling. In contrast, the pinball loss is related to the quan...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 36; H. 5; S. 984 - 997
Hauptverfasser:	Xiaolin Huang, Lei Shi, Suykens, Johan A. K.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Los Alamitos, CA IEEE 01.05.2014 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Classification Classifier design and evaluation Classifiers Computer science; control theory; systems Data processing. List processing. Character string processing Exact sciences and technology Fasteners Hinges Kernel Loss measurement Memory organisation. Data processing Noise Optimization Quantiles Regression Robustness Software Support vector machines Theoretical computing pinball loss support vector machine Classification Function evaluation Data analysis Stability Vector support machine Noise immunity Sampling Computational complexity Quantile Signal to noise ratio
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online-Zugang:	Volltext
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