Show/Hide Menu
Hide/Show Apps
anonymousUser
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Videos
Videos
Thesis submission
Thesis submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Contact us
Contact us
CobART: Correlation Based Adaptive Resonance Theory
Date
2009-06-26
Author
YAVAŞ, mustafa
Alpaslan, Ferda Nur
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
8
views
0
downloads
Cite This
This paper introduces a new type of ART 2 network that performs satisfactory categorization for a domain where the patterns are constructed from consecutive analog inputs. The main contribution relies on the correlation analysis methods used for category-matching. The resulting network model is named as Correlation Based Adaptive Resonance Theory (CobART). Correlation waveform analysis and Euclidian distance methods are used to elicit correlation between the learned categories and the data fed to the network.
Subject Keywords
Pattern classification
,
Correlation analysis
,
Adaptive resonance theory
URI
https://hdl.handle.net/11511/33295
DOI
https://doi.org/10.1109/med.2009.5164632
Collections
Department of Computer Engineering, Conference / Seminar
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
m. YAVAŞ and F. N. Alpaslan, “CobART: Correlation Based Adaptive Resonance Theory,” 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/33295.