Modelling functions from sample data with classification applications

In this thesis we investigate various aspects of the pattern recognition problem solving process. Pattern recognition can be viewed as a decision making process where the underlying density functions or discriminant functions of the application have to be estimated often in a high dimensional space. We consider two main types of estimators: the feed-forward neural network and the nearest neighbor method.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Saarinen, Sirpa Helena

• Artificial Intelligence
• Computer Science

Statement dc:rights

• Copyright 1994 Saarinen, Sirpa Helena

Language dc:language

eng

Identifier

AAI9512536
(UMI)AAI9512536

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/19668