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Similarity learning based on multiple support vector data description | IEEE Conference Publication | IEEE Xplore

Similarity learning based on multiple support vector data description


Abstract:

Similarity learning ranges over an extensive field in machine learning and pattern recognition. This paper deals with similarity learning based on multiple support vector...Show More

Abstract:

Similarity learning ranges over an extensive field in machine learning and pattern recognition. This paper deals with similarity learning based on multiple support vector data description (SVDD). It is well known that SVDD was proposed for one-class or two-class unbalanced learning problems. Thus, we propose a multiple SVDD (MSVDD) algorithm and apply it to multi-class learning problems. A SVDD model is trained by similar pairwise samples in the same class instead of all similar ones. In addition, the dissimilar pairwise samples are not considered in MSVDD. Experimental results validate that MSVDD is promising in similarity learning.
Date of Conference: 12-17 July 2015
Date Added to IEEE Xplore: 01 October 2015
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Conference Location: Killarney, Ireland

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