Iterative support vector machine with guaranteed accuracy and run time |
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Authors: | Qiaolin Ye Chunxia Zhao Ning Ye Yannan Chen |
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Affiliation: | 1. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China Email: yeqiaolin65620868@163.com;2. School of Information technology, Nanjing Forestry University, Nanjing 210094, China |
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Abstract: | Abstract: Using a conjugate gradient method, a novel iterative support vector machine (FISVM) is proposed, which is capable of generating a new non‐linear classifier. We attempt to solve a modified primal problem of proximal support vector machine (PSVM) and show that the solution of the modified primal problem reduces to solving just a system of linear equations as opposed to a quadratic programming problem in SVM. This algorithm not only has no requirement for special optimization solvers, such as linear or quadratic programming tools, but also guarantees fast convergence. The full algorithm merely needs four lines of MATLAB codes, which gives results that are similar to or better than that of several new learning algorithms, in terms of classification accuracy. Besides, the proposed stand‐alone approach is capable of dealing with instability of classification performance of smooth support vector machine, generalized proximal support vector machine, PSVM and reduced support vector machine. Experiments carried out on UCI datasets show the effectiveness of our approach. |
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Keywords: | Support vector machine conjugate gradient method iterative algorithm instability |
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