Semi-supervised learning in exemplar based neural networks

Keywords

Neural networks (Computer science); Supervised learning (Machine learning)

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

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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 p.

Language

English

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

Identifier

DP0029106

Subjects

Dissertations, Academic -- Engineering; Engineering -- Dissertations, Academic

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