TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Sample-based regularization for support vector machine classification

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoProceedings of the 7th International Conference on Image Processing Theory, Tools and Applications, IPTA 2017
KustantajaIEEE
Sivut1-6
Sivumäärä6
ISBN (elektroninen)9781538618417
DOI - pysyväislinkit
TilaJulkaistu - 8 maaliskuuta 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Conference on Image Processing Theory, Tools and Applications - Montreal, Kanada
Kesto: 28 marraskuuta 20171 joulukuuta 2017

Conference

ConferenceInternational Conference on Image Processing Theory, Tools and Applications
MaaKanada
KaupunkiMontreal
Ajanjakso28/11/171/12/17

Tiivistelmä

In this paper, we propose a new regularization scheme for the well-known Support Vector Machine (SVM) classifier that operates on the training sample level. The proposed approach is motivated by the fact that Maximum Margin-based classification defines decision functions as a linear combination of the selected training data and, thus, the variations on training sample selection directly affect generalization performance. We show that the exploitation of the proposed regularization scheme is well motivated and intuitive. Experimental results show that the proposed regularization scheme outperforms standard SVM in human action recognition tasks as well as classical recognition problems.

Tutkimusalat

Julkaisufoorumi-taso