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http://hdl.handle.net/11375/22658
Title: | Pattern Recognition of Arabic/Persian Handwritten Digits using Adaptive Boosting, Neural Networks and Deep Boltzmann Machines |
Authors: | Alattas, Sarah |
Advisor: | McNicholas, Paul D |
Department: | Mathematics and Statistics |
Publication Date: | 2017 |
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. |
URI: | http://hdl.handle.net/11375/22658 |
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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