Large margin classifiers such as support vector machines (SVM) have been applied successfully in various classification ***,their performance may be significantly degraded in the presence of *** this paper,we propose ...
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Large margin classifiers such as support vector machines (SVM) have been applied successfully in various classification ***,their performance may be significantly degraded in the presence of *** this paper,we propose a robust SVM formulation which is shown to be less sensitive to *** key idea is to employ an adaptively weighted hinge loss that explicitly incorporates outlier filtering in the SVM training,thus performing outlier filtering and classification *** resulting robust SVM formulation is *** first relax it into a semi-definite programming which admits a global *** improve the efficiency,an iterative approach is *** have performed experiments using both synthetic and real-world *** show that the performance of the standard SVM degrades rapidly when more outliers are included,while the proposed robust SVM training is more stable in the presence of outliers.
Recently, cryptographic applications based on finite fields have attracted much attention. The most demanding finite field arithmetic operation is multiplication. This investigation proposes a new multiplication algor...
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Recently, cryptographic applications based on finite fields have attracted much attention. The most demanding finite field arithmetic operation is multiplication. This investigation proposes a new multiplication algorithm over GF（2^m） using the dual basis representation. Based on the proposed algorithm, a parallel-in parallel-out systolic multiplier is presented, The architecture is optimized in order to minimize the silicon covered area （transistor count）. The experimental results reveal that the proposed bit-parallel multiplier saves about 65% space complexity and 33% time complexity as compared to the traditional multipliers for a general polynomial and dual basis of GF（2^m）.
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