Template-Type: ReDIF-Paper 1.0
Author-Name: Nalbantov, G.I.
Author-Name-Last: Nalbantov
Author-Name-First: Georgi
Author-Name: Bioch, J.C.
Author-Name-Last: Bioch
Author-Name-First: Cor
Author-Name: Groenen, P.J.F.
Author-Name-Last: Groenen
Author-Name-First: Patrick
Author-Person: pgr229
Title: Classification with support hyperplanes
Abstract: A new classification method is proposed, called Support Hy- perplanes (SHs). To solve the binary classification task, SHs consider the set of all hyperplanes that do not make classification mistakes, referred to as semi-consistent hyperplanes. A test object is classified using that semi-consistent hyperplane, which is farthest away from it. In this way, a good balance between goodness-of-fit and model complexity is achieved, where model complexity is proxied by the distance between a test object and a semi-consistent hyperplane. This idea of complexity resembles the one imputed in the width of the so-called margin between two classes, which arises in the context of Support Vector Machine learning. Class overlap can be handled via the introduction of kernels and/or slack vari- ables. The performance of SHs against standard classifiers is promising on several widely-used empirical data sets.
Creation-Date: 2006-07-19
File-URL: https://repub.eur.nl/pub/8012/ei2006-42.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: EI 2006-42
Keywords: Kernel methods, large margin and instance-based classifiers
Handle: RePEc:ems:eureir:8012