Abstract of FKI-188-94

Document-Name:  fki-188-94.ps.gz
Title:		Pattern Classification with Adaptive Distance Measures
Authors:	Gabriele Scheler 
Revision-Date:	1994/27/01
Category:	Technical Report (Forschungsberichte Künstliche Intelligenz)
Abstract:	In this paper, we want to explore the notion of learning 
		the classification of patterns from examples by synthesizing 
                distance functions.
		A working implementation of a distance classifier is presented.
		Its operation is illustrated with the problem of 
		classification according to parity (highly non-linear) and a 
		classification of feature vectors which involves dimension 
		reduction (a linear problem). 
		A solution to these problems is sought in two steps: (a) a 
		parametrized distance function (called a `distance function 
		scheme') is chosen, (b) setting parameters to values
		according to the classification of training patterns 
		results in a specific distance function. This induces a 
		classification on all remaining patterns.
		The general idea of this approach is to find restricted 
		functional shapes in order to model certain cognitive 
		functions of classification exactly, i.e. performing 
		classifications that occur as well as excluding classifications
		that do not naturally occur and may even be experimentally 
		proven to be excluded from learnability by a living organism.
		There are also certain technical advantages in using 
		restricted function shapes and simple learning rules, such as 
		reducing learning time, generating training sets and 
		individual patterns to set certain parameters, determining
		the learnability of a specific problem with a given 
		function scheme or providing additions to functions for 
		individual exceptions, while retaining the general
		shape for generalization.
Keywords:       pattern classification, distance functions, feature selection,
		linear perceptron, back-propagation, learning procedures, 
		Boolean functions 
Size:		25 pages
Language:	English
ISSN:		0941-6358
Copyright:	The ``Forschungsberichte Künstliche Intelligenz''
		series includes primarily preliminary publications,
		specialized partial results, and supplementary
		material. In the interest of a subsequent final
		publication these reports should not be copied. All
		rights and the responsability for the contents of the
		report are with the authors, which would appreciate
		critical comments.

Gerhard Weiss