Show/Hide Menu
Hide/Show Apps
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
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Tagged potential field extension to self organizing feature maps
Date
1998-04-23
Author
Baykal, Nazife
Erkmen, Aydan Müşerref
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
37
views
0
downloads
Cite This
This paper proposes an escape methodology to the local minima problem of self organizing feature maps generated in the overlapping regions which are equidistant to the corresponding winners. Two new versions of self organizing feature map are derived equipped with such a methodology. The first approach introduces an excitation term, which increases the convergence speed and efficiency of the algorithm while increasing the probability of escaping from local minima. In the second approach we associate a learning set which specifies attractive and repulsive field of output neurons. Results indicate that accuracy percentile of the new methods are higher than the original algorithm while they have the ability to escape from local minima.
Subject Keywords
Self organizing feature map
,
Neural modeling
,
Local minima avoidance
,
Classification
URI
https://hdl.handle.net/11511/55261
Conference Name
2nd International Conference on Knowledge-Based Intelligent Electronic Systems (KES 98)
Collections
Graduate School of Informatics, Conference / Seminar
Suggestions
OpenMETU
Core
Extended self organizing feature map: A tagged potential field approach
Baykal, Nazife (KLUWER ACADEMIC PUBL, SPUIBOULEVARD 50, PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS, 1999-8)
This paper proposes an escape methodology to the local minima problem of self organizing feature maps generated in the overlapping regions which are equidistant to the corresponding winners. Two new versions of the Self Organizing Feature Map are derived equipped with such a methodology. The first approach introduces an excitation term, which increases the convergence speed and efficiency of the algorithm, while increasing the probability of escaping from local minima. In the second approach, we associate a...
Edge-Aware Stereo Matching with O(1) Complexity
Cigla, Cevahir; Alatan, Abdullah Aydın (2012-01-26)
In this paper, a novel local stereo matching algorithm is introduced, providing precise disparity maps with low computational complexity. Following the common steps of local matching methods, namely cost calculation, aggregation, minimization and occlusion handling; the time consuming intensity dependent aggregation procedure is improved in terms of both speed and precision. For this purpose, a novel approach, denoted as permeability filtering (PF), is introduced, engaging computationally efficient two pass...
Efficient Edge-Preserving Stereo Matching
Cigla, Cevahir; Alatan, Abdullah Aydın (2011-11-13)
A computationally efficient stereo matching algorithm is introduced providing high precision dense disparity maps via local aggregation approach. The proposed algorithm exploits a novel paradigm, namely separable successive weighted summation (SWS) among horizontal and vertical directions with constant operational complexity, providing effective connected 2D support regions based on local color similarities. The intensity adaptive aggregation enables crisp disparity maps which preserve object boundaries and...
Compressed images for affinity prediction-2 (CIFAP-2): an improved machine learning methodology on protein-ligand interactions based on a study on caspase 3 inhibitors
Erdas, Ozlem; Andac, Cenk. A.; Gurkan-Alp, A. Selen; Alpaslan, Ferda Nur; Buyukbingol, Erdem (Informa UK Limited, 2015-01-01)
The aim of this study is to propose an improved computational methodology, which is called Compressed Images for Affinity Prediction-2 (CIFAP-2) to predict binding affinities of structurally related protein-ligand complexes. CIFAP-2 method is established based on a protein-ligand model from which computational affinity information is obtained by utilizing 2D electrostatic potential images determined for the binding site of protein-ligand complexes. The quality of the prediction of the CIFAP-2 algorithm was ...
Privacy-preserving horizontal federated learning methodology through a novel boosting-based federated random forest algorithm
Gençtürk, Mert; Çiçekli, Fehime Nihan; Department of Computer Engineering (2023-1-04)
In this thesis, a novel federated ensemble classification algorithm for horizontally partitioned data called Boosting-based Federated Random Forest (BOFRF) is proposed, which not only increases the predictive power of all participating sites, but also provides significantly high improvement on the predictive power of sites having unsuccessful local models. In this regard, a federated version of random forest, which is a well-known bagging algorithm, is implemented by adapting the idea of boosting to it. In ...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
N. Baykal and A. M. Erkmen, “Tagged potential field extension to self organizing feature maps,” presented at the 2nd International Conference on Knowledge-Based Intelligent Electronic Systems (KES 98), Adelaide, AUSTRALIA, 1998, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55261.