Hard-margin support vector machines (HM-SVMs) suffer from getting overfitting in the presence of noise. Soft-margin SVMs deal with this problem by introducing a regularization term...
Abstract. 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
—A fast online algorithm OnlineSVMR for training Ramp-Loss Support Vector Machines (SVMR s) is proposed. It finds the optimal SVMR for t+1 training examples using SVMR built on t...
In this paper we eliminate the need for parameter estimation associated with the set covering machine (SCM) by directly minimizing generalization error bounds. Firstly, we consider...
In this paper, we analyze the convergence of an iterative selftraining semi-supervised support vector machine (SVM) algorithm, which is designed for classi cation in small trainin...