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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...
In this paper, a variant of support vector novelty detection (SVND) with dot product kernels is presented for non-spherical distributed data. Firstly we map the data in input space into a reproducing kernel Hilbert space (RKHS) by using kernel trick. Secondly we perform whitening process on the mapped data using kernel principal component analysis (KPCA). Finally, we adopt SVND method to train and...
Support vector machine, a universal method for learning from data, gains its development based on statistical learning theory. It shows many advantages in solving nonlinearly small sample and high dimensional problems of pattern recognition. Only a part of samples or support vectors (SVs) plays an important role in the final decision function. But SVs could not be obtained in advance until a quadratic...
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