an interior point algorithm for convex quadratic programming problem with box constraints 框式約束凸二次規(guī)劃問題的內(nèi)點(diǎn)算法
in order to improve the efficiency of the algorithm, we not only correct some defects of the primal-dual interior point algorithm in [ 4 ], but also give a modified primal-dual interior point algorithm for convex quadratic programming problem with box constraints 為提高算法的有效性,對文[4]所給的原始-對偶內(nèi)點(diǎn)算法理論上的某些缺陷加以更正,并給出框式約束凸二次規(guī)劃問題的一個修正原始-對偶內(nèi)點(diǎn)算法。
based on the statistical learning theory and optimization theory, svms have been successfully applied to many fields such as pattern recognition, regression and etc . training an svm amounts to solving a convex quadratic programming problem . in this paper we do some researches on svms by the optimization theory and method 它將機(jī)器學(xué)習(xí)問題轉(zhuǎn)化為求解最優(yōu)化問題,并應(yīng)用最優(yōu)化理論構(gòu)造算法來解決問題,本文主要是從最優(yōu)化理論和算法的角度對支持向量機(jī)中的最優(yōu)化問題進(jìn)行研究。