The most attractive property of GS-SVMs is that its computational complexity scales quadratically with the size of training samples in the worst case. Extensive ...
In this paper, an alternative method, greedy stagewise algorithm, named GS-SVMs is presented to deal with the overfitting of HM-SVMs without the introduction of ...
Abstract. Hard margin support vector machines (HM-SVMs) have a risk of get- ting overfitting in the presence of the noise. Soft margin SVMs deal with this.
The most attractive property of GS-SVMs is that its computational complexity scales quadratically with the size of training samples in the worst case. Extensive ...
Aug 31, 2008 · Abstract: Hard-margin support vector machines (HM-SVMs) suffer from getting overfitting in the presence of noise. Soft-margin SVMs deal with ...
Hard margin support vector machines (HM-SVMs) have a risk of getting overfitting in the presence of the noise. Soft margin SVMs deal with this problem by ...
Liefeng Bo, Ling Wang, Licheng Jiao: Training Support Vector Machines Using Greedy Stagewise Algorithm. PAKDD 2005: 632-638. manage site settings.
In this paper, an al- ternative method, greedy stagewise algorithm for SVMs, named. GS-SVMs, is presented to cope with the overfitting of HM-SVMs without ...
Hence, we propose a greedy stagewise based algorithm to approximately train HSVR. At each iteration, the sample which has the maximal predicted discrepancy is ...
However, this disposal leads to a relatively high computational cost. In this paper, an alternative method, greedy stagewise algorithm, named GS-SVMs is ...