Classification with SVM in Lorentzian space

Classification is one of the most researched issues in Machine Learning. In this study, the Lorentzian Support Vector Machine (LSVM) method is proposed that performs classification in Lorentzian space. This proposed new classifier forms a hyperplane separating the classes based on the Lorentzian metric and maximize margins between nearest points to the hyperplane according to the Lorentzian distance. Thus, samples from different classes are classified in Lorentzian space. Also, for the purpose of increasing the classification accuracy, a pre-preprocessing is applied. Experimental results taken from LSVT, SONAR, TELESCOPE and WISCONSIN data sets validate the proposed LSVM method.