count instead of explicitly combines features. By setting with polynomial kernel degree (., d), different number of feature conjunctions can be imKernel methods such as support vector maplicitly computed. In this way, polynomial kernel chines (SVMs) have attracted a great deal SVM is often better than linear kernel which did of popularity in the machine learning and not use feature conjunctions. However, the training natural language processing (NLP) comand testing time costs for polynomial kernel SVM munities. .
