Title :
Classification with Lorentzian distance metric
Author :
Bilge, Hasan Sakir ; Kerimbekov, Yerzhan
Author_Institution :
Bilgisayar Muhendisligi Bolumu, Gazi Univ., Ankara, Turkey
Abstract :
In this study, a new distance metric that can be used in classification is proposed and its success is investigated. The classification success is increased by using the special distance metric in Lorentzian space. In order to find the optimum angle in transformation from Euclidean space to Lorentzian space, the decision line from Support Vector Machine is utilized. In experimental studies, the success of the proposed method is investigated by using open source data sets; WINE, ECOLI, BLOGGER, DIABET.
Keywords :
image classification; support vector machines; visual databases; BLOGGER; DIABET; ECOLI; Euclidean space; Lorentzian distance metric; Lorentzian space; SVM; WINE; decision line; open source data sets; optimum angle; support vector machine; Blogs; Extraterrestrial measurements; Geometry; Scientific computing; Tensile stress; Visualization; Lorentz space; classification; distance measure;
Conference_Titel :
Signal Processing and Communications Applications Conference (SIU), 2015 23th
Conference_Location :
Malatya
DOI :
10.1109/SIU.2015.7130286
