Evolving Neural Networks for Statistical Decision Theory

Michal Valko (Školiteľ: Mgr. Radoslav Harman, PhD.) | pridané: 15. júna 2005

Abstrakt diplomovej práce:

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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Diplomovú prácu poskytne autor na záujemcom na požiadanie (pošlite autorovi správu cez doleuvedený formulár).

Plný text diplomovej práci je k dispozícii na internete:
www.cs.pitt.edu/ ~michal/ projects/ thesis/ nesdt.pdf

Diplomová práca sa nachádza v knižnici tejto vysokej školy:
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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Bibliografický odkaz

VALKO, Michal: Evolving Neural Networks for Statistical Decision Theory [ Diplomová práca ] Univerzita Komenského, Fakulta matematiky, fyziky a informatiky, Katedra aplikovanej informatiky. Školiteľ: Mgr. Radoslav Harman, PhD.. Rok obhajoby: 2005

Diploma Thesis:

Evolving Neural Networks for Statistical Decision Theory

Michal Valko (Supervisor: Mgr. Radoslav Harman, PhD.) | added: 15. júna 2005

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.