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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Vydáno v:	IEEE transactions on pattern analysis and machine intelligence Ročník 36; číslo 5; s. 984 - 997
Hlavní autoři:	Xiaolin Huang, Lei Shi, Suykens, Johan A. K.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Los Alamitos, CA IEEE 01.05.2014 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
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Popis
Shrnutí:	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 quantile distance and the result is less sensitive. The pinball loss has been deeply studied and widely applied in regression but it has not been used for classification. In this paper, we propose a SVM classifier with the pinball loss, called pin-SVM, and investigate its properties, including noise insensitivity, robustness, and misclassification error. Besides, insensitive zone is applied to the pin-SVM for a sparse model. Compared to the SVM with the hinge loss, the proposed pin-SVM has the same computational complexity and enjoys noise insensitivity and re-sampling stability.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0162-8828 1939-3539 2160-9292 1939-3539
DOI:	10.1109/TPAMI.2013.178