Extension of General Convergence Framework with Significant Samples
Nguyen Vo, Yonggwan Won · ITC-CSCC :International Technical Conference on Circuits Systems, Computers and Communications · 2007
Single Class Classification is the problem of distinguishing one class of data (called positive class) from the universal set of multiple classes (negative class). In this paper, we proposed an improvement of Extended General Mapping Convergence framework using extreme learning machine, a recently developed machine learning algorithm. This proposed method keeping the high accuracy in classification while improving the high speed of old method.