Genetic Algorithm with Species for Regularization Network Metalearning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F10%3A00348394" target="_blank" >RIV/67985807:_____/10:00348394 - isvavai.cz</a>

Result on the web

—

DOI - Digital Object Identifier

—

Result language

angličtina

Original language name

Genetic Algorithm with Species for Regularization Network Metalearning

Original language description

Regularization networks are one of the important methods for supervised learning. They benefit from very good theoretical background, although the presence of metaparameters is their drawback. The metaparameters are typically supposed to be given in advance and come ready as an input of the algorithm. Typically, they are set based on the task context by an experienced user. In this paper, we develop a method for finding optimal values of metaparameters, namely the type of kernel function, kernel parameters and regularization parameter. The method is based on co-evolutionary genetic algorithms with different species for different kind

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

IN - Informatics

OECD FORD branch

—

Project

<a href="/en/project/KJB100300804" target="_blank" >KJB100300804: Neural Networks Learning Algorithms Based on Regularization Theory</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2010

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Informačné Technológie - Aplikácie a Teória

ISBN

978-80-970179-3-4

ISSN

—

e-ISSN

—

Number of pages

6

Pages from-to

—

Publisher name

Pont

Place of publication

Seňa

Event location

Smrekovica

Event date

Sep 21, 2010

Type of event by nationality

EUR - Evropská akce

UT code for WoS article

—