Yiǧilmiş genelleme algoritmasi için doǧrusal ayrilabilirlik analizi

Translated title of the contribution: Linear separability analysis for stacked generalization architecture

Mete Ozay, Fatoş T.Yarman Vural

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Stacked Generalization algorithm aims to increase the individual classification performances of the classifiers by combining the information obtained from various classifiers in a multilayer architecture by either linear or nonlinear techniques. Performance of the algorithm varies depending on the application domains and the space analyses that affect the classification performances could not be applied successfully. In the present work, linear and nonlinear transformations are investigated within and between each layer, and the linear separability property of the architecture is examined. In the conclusion of the analyses, it is observed that the data space can be separated linearly.

Translated title of the contributionLinear separability analysis for stacked generalization architecture
Original languageTurkish
Title of host publication2009 IEEE 17th Signal Processing and Communications Applications Conference, SIU 2009
Pages1009-1012
Number of pages4
DOIs
Publication statusPublished - 2009 Oct 29
Event2009 IEEE 17th Signal Processing and Communications Applications Conference, SIU 2009 - Antalya, Turkey
Duration: 2009 Apr 92009 Apr 11

Publication series

Name2009 IEEE 17th Signal Processing and Communications Applications Conference, SIU 2009

Conference

Conference
CountryTurkey
CityAntalya
Period09/4/909/4/11

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Communication

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