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The SVDD (support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the feature space in order to distinguish a set of normal data from all other possible abnormal objects. The usual strategy of the SVDD depends on the process of finding the region for the normal-class training data with somewhat...
In this paper, we discuss subspace based least squares support vector machines (SSLS-SVMs), in which an input vector is classified into the class with the maximum similarity. Namely, we define the similarity measure for each class by the weighted sum of vectors called dictionaries and optimize the weights so that the margin between classes is optimized. Because the similarity measure is defined for...
For classification problem clustering method divides the dataset into many clusters based on correlation attribute of all elements of the dataset. How to classify data within the same clustering number as close as possible and data in different clusters as depart as possible is the key of clustering method. Clustering support vector machines (ClusterSVM) partition the training data into disjoint clusters...
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