Strojno učenje v porazdeljenem okolju z uporabo paradigme MapReduce

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Ključne besede

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Jezik:	Slovenski jezik
Leto izida:	2014
Tipologija:	2.09 - Magistrsko delo
Organizacija:	UL FRI - Fakulteta za računalništvo in informatiko
Založnik:	[R. Orač]
UDK:	004.85(043.2)
COBISS:	1536017347
Št. ogledov:	61
Št. prenosov:	6
Ocena:	0 (0 glasov)
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Ostali podatki

Sekundarni jezik:	Angleški jezik
Sekundarni naslov:	Machine learning algorithms in distributed environment with MapReduce paradigm
Sekundarni povzetek:	Implementation of machine learning algorithms in a distributed environment ensures us multiple advantages, like processing of large datasets and linear speedup with additional processing units. We describe the MapReduce paradigm, which enables distributed computing, and the Disco framework, which implements it. We present the summation form, which is a condition for efficient implementation of algorithms with the MapReduce paradigm, and describe the implementations of the selected algorithms. We propose novel distributed random forest algorithms that build models on subsets of the dataset. We compare time and accuracy of the algorithms with the well recognized data analytics tools. We end our master thesis by describing the integration of the implemented algorithms into the ClowdFlows platform, which is a web platform for construction, execution and sharing of interactive workflows for data mining. With this integration, we enabled processing of big batch data with visual programming.
Sekundarne ključne besede:	MapReduce;distributed computing;Disco;machine learning;DiscoMLL;distributed random forest;ClowdFlows;computer science;computer and information science;master's degree;
Vrsta datoteke:	application/pdf
Vrsta dela (COBISS):	Magistrsko delo/naloga
Študijski program:	1000471
Komentar na gradivo:	Univ. v Ljubljani, Fak. za računalništvo in informatiko
Strani:	123 str.
ID:	8739557