TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Maximum Margin Binary Classifiers using Intrinsic and Penalty graphs

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2016 24th European Signal Processing Conference (EUSIPCO)
KustantajaIEEE
Sivut2270-2274
Sivumäärä5
ISBN (elektroninen)978-0-9928-6265-7
TilaJulkaistu - 2016
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaEUROPEAN SIGNAL PROCESSING CONFERENCE -
Kesto: 1 tammikuuta 1900 → …

Julkaisusarja

Nimi
ISSN (elektroninen)2076-1465

Conference

ConferenceEUROPEAN SIGNAL PROCESSING CONFERENCE
Ajanjakso1/01/00 → …

Tiivistelmä

In this paper a variant of the binary Support Vector Machine classifier that exploits intrinsic and penalty graphs in its optimization problem is proposed. We show that the proposed approach is equivalent to a two-step process where the data is firstly mapped to an optimal discriminant space of the input space and, subsequently, the original SVM classifier is applied. Our approach exploits the underlying data distribution in a discriminant space in order to enhance SVMs generalization ability. We also extend this idea to the Least Squares SVM classifier, where the adoption of the intrinsic and penalty graphs acts as a regularizer incorporating discriminant information in the overall solution. Experiments on standard and recently introduced datasets verify our analysis since, in the cases where the classes forming the problem are not well discriminated in the original feature space, the exploitation of both intrinsic and penalty graphs enhances performance.

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