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Learning algorithms of the support vector machine is to map the input vector to a high dimensional space through certain kernel function and separate the image of the original linear input vector with the maximum of interval under consideration. This paper is about the limb motion recognition problem of stroke patients, mapping the input vector to the reproducing kernel RKHS (reproducing Kernel Hilbert...
Support vector machine (SVM) is a popular method for classification in data mining. The canonical duality theory provides a unified analytic solution to a wide range of discrete and continuous problems in global optimization. This paper presents a canonical duality approach for solving support vector machine problem. It is shown that by the canonical duality, these nonconvex and integer optimization...
The Gaussian radial basis function is widely used in the support vector machine (SVM) due to its attractive characteristics. The parameter (σ) in this kernel is crucial to robust performance of SVM. In this paper, we derive a formula to compute the optimal s under the principle of maximizing the class separability in the kernel space. The most attractive feature of the proposed method is that no optimization...
Kernel methods are widely used for document classification in diverse domains. Popular kernels such as bag-of-word kernels and tree kernels show satisfactory results in classifying documents such as articles, e-mails or web pages. However, they provide less satisfactory performances in classifying short-text documents since the short documents have insufficient feature space. In order to cope with...
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