Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/14101| Title: | Generating Learning Algorithms: Hidden Markov Models as a Case Study |
| Authors: | Szymczak, Daniel |
| Advisor: | Carette, Jacques |
| Department: | Software Engineering |
| Keywords: | code generation;hidden markov models;domain specific languages;machine learning;bayesian statistics;Other Engineering;Other Engineering |
| Publication Date: | Apr-2014 |
| 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> |
| URI: | http://hdl.handle.net/11375/14101 |
| Identifier: | opendissertations/8928 10005 5506238 |
| Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| fulltext.pdf | 675.7 kB | Adobe PDF | View/Open |
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