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

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

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.