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http://hdl.handle.net/11375/14101
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DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Carette, Jacques | en_US |
dc.contributor.author | Szymczak, Daniel | en_US |
dc.date.accessioned | 2014-06-18T17:06:19Z | - |
dc.date.available | 2014-06-18T17:06:19Z | - |
dc.date.created | 2014-04-21 | en_US |
dc.date.issued | 2014-04 | en_US |
dc.identifier.other | opendissertations/8928 | en_US |
dc.identifier.other | 10005 | en_US |
dc.identifier.other | 5506238 | en_US |
dc.identifier.uri | http://hdl.handle.net/11375/14101 | - |
dc.description.abstract | <p>This thesis presents the design and implementation of a source code generator for dealing with Bayesian statistics. The specific focus of this case study is to produce usable source code for handling Hidden Markov Models (HMMs) from a Domain Specific Language (DSL).</p> <p>Domain specific languages are used to allow domain experts to design their source code from the perspective of the problem domain. The goal of designing in such a way is to increase the development productivity without requiring extensive programming knowledge.</p> | en_US |
dc.subject | code generation | en_US |
dc.subject | hidden markov models | en_US |
dc.subject | domain specific languages | en_US |
dc.subject | machine learning | en_US |
dc.subject | bayesian statistics | en_US |
dc.subject | Other Engineering | en_US |
dc.subject | Other Engineering | en_US |
dc.title | Generating Learning Algorithms: Hidden Markov Models as a Case Study | en_US |
dc.type | thesis | en_US |
dc.contributor.department | Software Engineering | en_US |
dc.description.degree | Master of Applied Science (MASc) | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Size | Format | |
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fulltext.pdf | 675.7 kB | Adobe PDF | View/Open |
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