One Class SVM Vs SVM Classification

One class classification distinguishes the target class from all other classes using only training data from the target class. One class classification is suitable for those situations where outliers are not represented well in the training set. One-class learning, or unsupervised SVM, aims at separating data from the origin in the high-dimensional, predictor space (not the original predictor space), and is an algorithm used for outlier detection. Support vector machine is a machine learning method that is widely used for data analyzing and pattern recognizing. Support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. In this paper we will review the difference between both these classes.