Title :
An application of fuzzy support vectors
Author :
Mill, John ; Inoue, Atsushi
Author_Institution :
Spokane Falls Community Coll., WA, USA
Abstract :
Support Vector Machines (SVMs) are a recently introduced Machine Learning technique. SVMs approach binary classification by attempting to find a hyperplane that separates the two categories of training vectors. This hyperplane is expressed as a function of a subset of the training vectors. These vectors are called support vectors. In this paper, we present a method of fuzzifying support vectors based off of the results of an SVM induction. We then propose a method of enhancing SVM induction using these fuzzy support vectors. We finish by presenting a computational example using the IRIS data set.
Keywords :
fuzzy set theory; learning (artificial intelligence); pattern classification; support vector machines; IRIS data set; SVM induction; binary classification; fuzzy support vectors; machine learning; subset; support vector machines; training vectors; Application software; Computer science; Educational institutions; Engines; Iris; Kernel; Machine learning; Milling machines; Support vector machine classification; Support vector machines;
Conference_Titel :
Fuzzy Information Processing Society, 2003. NAFIPS 2003. 22nd International Conference of the North American
Print_ISBN :
0-7803-7918-7
DOI :
10.1109/NAFIPS.2003.1226801
