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One of the main goals in machine learning is the general functional dependencies. Recent advances in kernel-based methods are focused on designing flexible and powerful input and output representations. This paper describes how rough set (RS) and support vector machine (SVM) can be practically implemented in ore-rock classification, and discusses the kernel mapping technique which is used to construct...
A new technique using a positive symmetric function to improve support vector machine (SVM) is presented. Firstly, the support vectors are obtained from traditional SVM. Secondly, a positive scalar function is constructed using the support vectors. Thirdly, a new kernel function is obtained from the Gaussian kernel multiplied by the positive symmetric function merged into data information. The new...
In this paper we propose a modified framework for support vector machines, called ellipsoid support vector machines (ESVMs), to improve classification capability. The principle of ESVMs is to use a minimum ellipsoid to enclose the specific patterns. Utilizing an approximation algorithm for the minimum enclosing ellipsoid problem in computational geometry allow ESVMs provided better performance than...
Support vector machines have been extensively used in machine learning because of its efficiency and its theoretical background. This paper focuses on nu-transductive support vector machines for classification (nu-TSVC) and construct a new algorithm - Unconstrained nu-Transductive Support Vector Machines (Unu-TSVM). After researching on the special construction of primal problem in nu-TSVM, we transform...
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