Generating Learning Algorithms: Hidden Markov Models as a Case Study

dc.contributor.advisor	Carette, Jacques	en_US
dc.contributor.author	Szymczak, Daniel	en_US
dc.contributor.department	Software Engineering	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.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.description.degree	Master of Applied Science (MASc)	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.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

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