NDLI: Ischemia detection using supervised learning for hierarchical neural networks based on kohonen-maps

Content Provider	IEEE Xplore Digital Library
Author	Vladutu, L. Papadimithou, S. Mavroudi, S. Bezerianos, A.
Copyright Year	2001
Description	Author affiliation: Med. Phys. Dept., Patras Univ., Greece (Vladutu, L.; Papadimithou, S.; Mavroudi, S.; Bezerianos, A.)
Abstract	The detection of ischemic episodes Is a difficult pattern classification problem. The motivation for developing the Supervising Network - Self Organizing Map (sNet-SOM) model is to design computationally effective solutions for the particular problem of ischemia detection and other similar applications. The sNet-SOM uses unsupervised learning for the regions where the classification is not ambiguous and supervised for the "difficult" ones-in a two-stage learning process. The unsupervised learning approach extends and adapts the Self-Organizing Map (SOM) algorithm of Kohonen. The basic SOM is modified with a dynamic expansion process controlled with an entropy based criterion that allows the adaptive formation of the proper SOM structure. This extension proceeds until the total number of training patterns that are mapped to neurons with high entropy (therefore with ambiguous classification) reduces to a size manageable numerically with a proper supervised model. The second learning phase (supervised training) has the objective of constructing better decision boundaries of the ambiguous regions. In this phase, a special supervised network is trained for the task of reduced computationally complexity- to perform the classification only of the ambiguous regions. After we tried with different classes of supervised networks, we obtained the best results with the Support Vector Machines (SVM) as local experts.
Starting Page	1688
Ending Page	1691
File Size	500966
Page Count	4
File Format	PDF
ISBN	0780372115
ISSN	1094687X
DOI	10.1109/IEMBS.2001.1020540
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-10-25
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Ischemic pain Supervised learning Neural networks Management training Computer networks Unsupervised learning Entropy Support vector machines Support vector machine classification Pattern classification
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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