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Support vector data description (SVDD) has become a very attractive kernel method due to its good results in many novelty detection problems. Similar to the support vector machine (SVM), the decision function of SVDD is also expressed in terms of the kernel expansion, which results in a run-time complexity linear in the number of support vectors. For applications where fast real-time response is needed,...
Many studies have been proposed to identify gene makers that are associated with cancers, but the found markers are approach dependent. For example, the results are correlated with classifiers in supervised feature selection, and many of them didn't consider the influences of other factors, such as the grades or stages of cancers. In this study, we proposed a supervised SVD approach to extract the...
In this paper, we propose a new efficient data reduction algorithm through combining lattice with rough set. On the basis of lattice learning, the algorithm applies the concept of attribute reduction in the theory of rough sets and calculates the importance degree of attributes automatically by a density based approach. Under acceptable classification precision and complexity, it reduces row and column...
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