Semi-supervised learning in exemplar based neural networks

Keywords

Neural networks (Computer science), Supervised learning (Machine learning), Semi-supervised learning algorithms, Exemplar-based classifiers, Training exemplar modification, Noise-robust classification, Model compression in neural networks

Abstract

A new and novel method for training exemplar based neural networks called Semisupervised learning is introduced. Semi-supervised learning (SSL) refers to the semisupervised manner according to which exemplars are formed during training to identify clusters. Instead of forcing a neural network to achieve a zero post training error a new learning scheme is introduced that would allow exemplars to occasionally misclassify training patterns by permitting training patterns, under certain circumstances, to modify exemplars associated with a different class. This allows the network to achieve a smooth learning curve without being disrupted by the noise in the dataset. The new technique was applied to 3 exemplar-based classifiers and results were found to be very promising. The new semi-supervised networks performed better classification and achieved better compression when compared to their supervised counterparts.

Notes

This item is only available in print in the UCF Libraries. If this is your thesis or dissertation, you can help us make it available online for use by researchers around the world by STARS for more information.

Graduation Date

2003

Advisor

Georgiopoulos, Michael

Degree

Master of Science (M.S.)

College

College of Engineering

Department

Electrical Engineering and Computer Science

Format

PDF

Pages

200 pages

Language

English

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

Identifier

DP0029106

Subjects

Dissertations; Academic -- Engineering; Engineering -- Dissertations; Academic; Neural networks (Computer science)--Models; Neural networks (Computer science)--Design and construction; Neural networks (Computer science)--Statistical methods; Machine learning--Technique; Machine learning--Experiments

This document is currently not available here.

Share

COinS
 

Accessibility Statement

This item was created or digitized prior to April 24, 2026, or is a reproduction of legacy media created before that date. It is preserved in its original, unmodified state specifically for research, reference, or historical recordkeeping. In accordance with the ADA Title II Final Rule, the University Libraries provides accessible versions of archival materials upon request. To request an accommodation for this item, please submit an accessibility request form.