3. Support Vector Machine Classification

Support vector machine (SVM) has been a popular technique in data analytics. Shi et al. [1] has reported some SVM algorithms. They vary from leave-one-out (LOO) bounds approaches, multi-class, unsupervised, semi-supervised and robust SVMs. Following the direction of the research afterwards, this Chapter provides five sections about advances of SVM in big data analytics. Section 3.1 has two subsections. The first one outlines the recent findings of the author’s research team on SVM [2] while the second one is about two new decomposition algorithms for training bound-constrained SVM [3]. Section 3.2 describes different twin SVM in classification with four subsections. The first one explores the improved twin SVM [4]. The second one is extending twin SVM for multi-category classification problems [5]. The third one provides robust twin SVM for pattern classification [6]. The fourth one elaborates structural twin SVM for classification [7]. Section 3.3 shows nonparallel SVM with four subsections. The first one is about a nonparallel SVM for a classification problem with universum learning [8]. The second one is about a divide-and-combine method for large scale nonparallel SVM [9]. The third one explores nonparallel SVM for pattern classification [4]. The fourth one is a multi-instance learning algorithm based on nonparallel classifier [10]. Section 3.4 shows Laplacian SVM classifiers with two subsections. One is about successive overrelaxation for Laplacian SVM [11] while another one is about Laplacian twin SVM for semi-supervised classification [12]. Finally, Sect. 3.5 discusses loss functions of SVM classification with three subsections. The first one is about the ramp loss least squares SVM [13]. The second is about the ramp loss nonparallel SVM for pattern classification [14]. The third one is about a classification model using privileged information and its application [10].