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http://hdl.handle.net/11375/22658
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DC Field | Value | Language |
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dc.contributor.advisor | McNicholas, Paul D | - |
dc.contributor.author | Alattas, Sarah | - |
dc.date.accessioned | 2018-03-16T19:19:59Z | - |
dc.date.available | 2018-03-16T19:19:59Z | - |
dc.date.issued | 2017 | - |
dc.identifier.uri | http://hdl.handle.net/11375/22658 | - |
dc.description.abstract | Classification of handwritten numerals has captured the attention of the statistical and machine learning community. A common statistical approach is ensemble learning, which combines basic classifiers to produce one powerful predictor. Another popular method is neural networks, which is a non-linear two stage statistical model inspired by the biological neural networks. In this thesis, neural networks (nnet), adaptive boosting (AdaBoost), and deep Boltzmann machine (DBM) algorithms are tested for Arabic/Persian handwritten digits, and their recognition performance is then compared. | en_US |
dc.language.iso | en | en_US |
dc.title | Pattern Recognition of Arabic/Persian Handwritten Digits using Adaptive Boosting, Neural Networks and Deep Boltzmann Machines | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Mathematics and Statistics | en_US |
dc.description.degreetype | Thesis | en_US |
dc.description.degree | Master of Science (MSc) | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Alattas_Sarah_Ali_201710_Msc.pdf | 1.62 MB | Adobe PDF | View/Open |
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