In this paper,a successive linearization algorithm is introduced for solving linear complementarity *** algorithm is given by *** for solving the NP-hard absolute value *** utilizing an equivalence relation to the lin...
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In this paper,a successive linearization algorithm is introduced for solving linear complementarity *** algorithm is given by *** for solving the NP-hard absolute value *** utilizing an equivalence relation to the linear complementarity problem,we give some numerical results for the linear complementarity problem.
Misclassification minimization is an important and interesting topic in classification problem. Obviously, exploring the solution for this topic will benefit to many real life problems, such as credit card clients cla...
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ISBN:
(纸本)9781479942749
Misclassification minimization is an important and interesting topic in classification problem. Obviously, exploring the solution for this topic will benefit to many real life problems, such as credit card clients classification. This paper focuses on misclassification minimization based on multiple criteria linear programming (MCLP), proposing two different schemes to minimize the number of misclassified points in original MCLP. Especially, the complementarity is used to construct the first scheme and linear approximation technique is applied to solve it. Furthermore, successive linearization algorithm (SLA) is employed to achieve minimization the second scheme. Finally, numerical experiment tests the effect of this idea.
The multiple instance classification problem (Dietterich et al., Artif. Intell. 89:31-71, 1998;Auer, Proceedings of 14th International Conference on Machine Learning, pp. 21-29, Morgan Kaufmann, San Mateo, 1997;Long e...
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The multiple instance classification problem (Dietterich et al., Artif. Intell. 89:31-71, 1998;Auer, Proceedings of 14th International Conference on Machine Learning, pp. 21-29, Morgan Kaufmann, San Mateo, 1997;Long et al., Mach. Learn. 30(1):7-22, 1998) is formulated using a linear or nonlinear kernel as the minimization of a linear function in a finite-dimensional (noninteger) real space subject to linear and bilinear constraints. A linearizationalgorithm is proposed that solves a succession of fast linear programs that converges in a few iterations to a local solution. Computational results on a number of datasets indicate that the proposed algorithm is competitive with the considerably more complex integer programming and other formulations. A distinguishing aspect of our linear classifier not shared by other multiple instance classifiers is the sparse number of features it utilizes. In some tasks, the reduction amounts to less than one percent of the original features.
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