Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/13584
Title: Multi-classifier system for robust pattern recognition
Authors: Ng, Geok See.
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
DRNTU::Engineering::Computer science and engineering::Theory of computation::Analysis of algorithms and problem complexity
Issue Date: 1999
Abstract: Despite the success of many pattern recognition problems in a constrained domain, the task of pattern recognition is "ill-defined" and difficult due to the noise and large variations in input data. A promising approach is to use several classifiers simultaneously, such that they can complement each other in correctness. This thesis tackles the recognition problem in two aspects: (1) propose a new classifier called Contender Network (CN) and (2) propose a combining classifier called Combined Classifier (CC) which aggregates the outputs of a number of pattern classifiers using a new evidence combination method. So the primary objective of this work is to propose an effective framework of multiple classifier system that takes advantage of the strength of the individual classifier. This framework is then applied to the task of recognition of hand-written numeric digits.
URI: http://hdl.handle.net/10356/13584
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Theses

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