非均衡加權(quán)隨機(jī)梯度下降SVM在線算法
發(fā)布時(shí)間:2018-08-04 07:46
【摘要】:隨機(jī)梯度下降(stochastic gradient descent,SGD)方法已被應(yīng)用于大規(guī)模支持向量機(jī)(support vector machine,SVM)訓(xùn)練,其在訓(xùn)練時(shí)采取隨機(jī)選點(diǎn)的方式,對(duì)于非均衡分類(lèi)問(wèn)題,導(dǎo)致多數(shù)類(lèi)點(diǎn)被抽取到的概率要遠(yuǎn)遠(yuǎn)大于少數(shù)類(lèi)點(diǎn),造成了計(jì)算上的不平衡。為了處理大規(guī)模非均衡數(shù)據(jù)分類(lèi)問(wèn)題,提出了加權(quán)隨機(jī)梯度下降的SVM在線算法,對(duì)于多數(shù)類(lèi)中的樣例被賦予較小的權(quán)值,而少數(shù)類(lèi)中的樣例被賦予較大的權(quán)值,然后利用加權(quán)隨機(jī)梯度下降算法對(duì)SVM原問(wèn)題進(jìn)行求解,減少了超平面向少數(shù)類(lèi)的偏移,較好地解決了大規(guī)模學(xué)習(xí)中非均衡數(shù)據(jù)的分類(lèi)問(wèn)題。
[Abstract]:Stochastic gradient descent (stochastic gradient descenting (stochastic gradient) method has been applied to large-scale support vector machine (support vector machine) training. The probability of extracting most of the points is much higher than that of a few, which results in the imbalance of calculation. In order to deal with the problem of large-scale disequilibrium data classification, a weighted stochastic gradient descent SVM online algorithm is proposed, in which the sample in most classes is given a smaller weight, while the sample in a few classes is given a larger weight. Then the weighted stochastic gradient descent algorithm is used to solve the original SVM problem, which reduces the deviation of the superplane for a few classes and solves the classification problem of unbalanced data in large-scale learning.
【作者單位】: 河北大學(xué)數(shù)學(xué)與信息科學(xué)學(xué)院河北省機(jī)器學(xué)習(xí)與計(jì)算智能重點(diǎn)實(shí)驗(yàn)室;
【基金】:河北省自然科學(xué)基金No.F2015201185~~
【分類(lèi)號(hào)】:TP181
本文編號(hào):2163139
[Abstract]:Stochastic gradient descent (stochastic gradient descenting (stochastic gradient) method has been applied to large-scale support vector machine (support vector machine) training. The probability of extracting most of the points is much higher than that of a few, which results in the imbalance of calculation. In order to deal with the problem of large-scale disequilibrium data classification, a weighted stochastic gradient descent SVM online algorithm is proposed, in which the sample in most classes is given a smaller weight, while the sample in a few classes is given a larger weight. Then the weighted stochastic gradient descent algorithm is used to solve the original SVM problem, which reduces the deviation of the superplane for a few classes and solves the classification problem of unbalanced data in large-scale learning.
【作者單位】: 河北大學(xué)數(shù)學(xué)與信息科學(xué)學(xué)院河北省機(jī)器學(xué)習(xí)與計(jì)算智能重點(diǎn)實(shí)驗(yàn)室;
【基金】:河北省自然科學(xué)基金No.F2015201185~~
【分類(lèi)號(hào)】:TP181
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