Evolving Neural Networks for Statistical Decision Theory: Master Thesis

Michal Valko 1
1 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : 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.
Document type :
Master thesis
Complete list of metadatas

Cited literature [22 references]  Display  Hide  Download

https://hal.inria.fr/hal-00646451
Contributor : Michal Valko <>
Submitted on : Wednesday, November 30, 2011 - 12:21:07 AM
Last modification on : Thursday, February 21, 2019 - 10:52:49 AM
Long-term archiving on : Friday, November 16, 2012 - 12:21:38 PM

File

nesdt.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00646451, version 1

Collections

Citation

Michal Valko. Evolving Neural Networks for Statistical Decision Theory: Master Thesis. Machine Learning [stat.ML]. 2005. ⟨hal-00646451⟩

Share

Metrics

Record views

296

Files downloads

250