Abstract
In this paper, we propose a stochastic gradient descent algorithm, called stochastic gradient descent method-based generalized pinball support vector machine (SG-GPSVM), to solve data classification problems. This approach was developed by replacing the hinge loss function in the conventional support vector machine (SVM) with a generalized pinball loss function. We show that SG-GPSVM is convergent and that it approximates the conventional generalized pinball support vector machine (GPSVM). Further, the symmetric kernel method was adopted to evaluate the performance of SG-GPSVM as a nonlinear classifier. Our suggested algorithm surpasses existing methods in terms of noise insensitivity, resampling stability, and accuracy for large-scale data scenarios, according to the experimental results.
Highlights
Introduction the Using Generalized Pinball LossSupport vector machine (SVM) is a popular supervised binary classification algorithm based on statistical learning theory
Compared to the hinge loss support vector machine (SVM) and Pegasos, the major advantage of our proposed method is that SG-generalized pinball support vector machine (GPSVM) is less sensitive to noise, especially the feature noise around the decision boundary
We investigated the convergence of SG-GPSVM and the theoretical approximation between GPSVM and SG-GPSVM
Summary
Support vector machine (SVM) is a popular supervised binary classification algorithm based on statistical learning theory. A modified e-insensitive zone for Pin-SVM was proposed This method does not consider the patterns that lie in the insensitive zone while building the classifier, and its formulation requires the value of e to be specified beforehand; a bad choice may affect its performance. Rastogi [17] recently proposed the modified (e1 , e2 )-insensitive zone support vector machine This method is an extension of existing loss functions that account for noise sensitivity and resampling stability. In order to overcome the above-mentioned limitations of large-scale problems and inspired by the studies of SVM and the generalized pinball loss function, we propose a novel stochastic subgradient descent method with generalized pinball support vector machine (SG-GPSVM).
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