Meta:
Michal Valko
Dostupný celý text práce:
WEB
r. 2005
Supervisor:
Mgr. Radoslav Harman, PhD.
Keywords:
decision theory, neural networks, genetic algorithms, JASTAP, spiking neuron models, information processing, neural modeling, decision systems, artificial intelligence, diploma thesis, master thesis
Science:
NATURAL SCIENCE » Infromatic science » Aplikovaná informatika » Umelá inteligencia
School:
Univerzita Komenského » Fakulta matematiky, fyziky a informatiky » Katedra aplikovanej informatiky
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Evolving Neural Networks for Statistical Decision Theory
Abstract of diploma thesis:
Real biological networks are able to make decisions. We will show that this behavior can be observed even in some simple architectures of biologically plausible neural models. The great interest of this thesis is also to contribute to methods of statistical decision theory by giving a lead how to evolve the neural networks to solve miscellaneous decision tasks.
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Source of diploma thesis:
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Full text of this diploma thesis is on internet address:
www.cs.pitt.edu/ ~michal/ projects/ thesis/ nesdt.pdf
Hard copy of diploma thesis is located at this university:
Univerzita Komenského - Fakulta matematiky, fyziky a informatiky - Katedra aplikovanej informatiky
Univerzita Komenského v Bratislave, Fakulta matematiky, fyziky a informatiky, Knižničné a edičné centrum
Mlynská dolina, Pavilón 1
Bratislava 4
842 41
http://www.fmph.uniba.sk/
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Bibliographic reference
VALKO, Michal: Evolving Neural Networks for Statistical Decision Theory [ Diploma Thesis ] Univerzita Komenského, Fakulta matematiky, fyziky a informatiky, Katedra aplikovanej informatiky. Supervisor: Mgr. Radoslav Harman, PhD.. Year of defense: 2005
Diploma Thesis:
Evolving Neural Networks for Statistical Decision Theory
Abstract of diploma thesis:
Real biological networks are able to make decisions. We will show that this behavior can be observed even in some simple architectures of biologically plausible neural models. The great interest of this thesis is also to contribute to methods of statistical decision theory by giving a lead how to evolve the neural networks to solve miscellaneous decision tasks.
Real biological networks are able to make decisions. We will show that this behavior can be observed even in some simple architectures of biologically plausible neural models. The great interest of this thesis is also to contribute to methods of statistical decision theory by giving a lead how to evolve the neural networks to solve miscellaneous decision tasks.