2026-09-18T19:59:53.182472800Zhttps://opus.uleth.ca/server/oai/requestoai:opus.uleth.ca:10133/32412019-02-07T06:11:42Zcom_10133_3346com_10133_1com_10133_296col_10133_3347col_10133_298
ULethbridge OPUS Repository
supervisor
Zhang, John Z.
author
Zaamout, Khobaib M
2013-02-05T17:15:01Z
2013-02-05T17:15:01Z
2012
https://hdl.handle.net/10133/3241
The task of pattern recognition is one of the most recurrent tasks that we encounter in
our lives. Therefore, there has been a significant interest of automating this task for many
decades. Many techniques have been developed to this end, such as neural networks. Neural
networks are excellent pattern classifiers with very robust means of learning and a relatively
high classification power. Naturally, there has been an increasing interest in further
improving neural networks’ classification for complex problems. Many methods have been
proposed.
In this thesis, we propose two novel ensemble approaches to further improving neural
networks’ classification power, namely paralleling neural networks and chaining neural
networks. The first seeks to improve a neural network’s classification by combining the
outputs of a set of neural networks together via another neural network. The second improves
a neural network’s accuracy by feeding the outputs of a neural network into another
and continually doing so in a chaining fashion until the error is reduced sufficiently. The
effectiveness of both approaches has been demonstrated through a series of experiments.
iv
en_US
Neural networks (Computer science)
Pattern recognition systems
Dissertations, Academic
Two novel ensemble approaches for improving classification of neural networks
Thesis
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URL
http://localhost:4000/bitstreams/f4826d65-a459-4e3c-ad26-256d6966a39c/download
File
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application/pdf
ZAAMOUT_KHOBAIB_MSC_2012.pdf
URL
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ZAAMOUT_KHOBAIB_MSC_2012.pdf.txt