Adaptive and fault tolerant medical vest for life-critical medical monitoring
Proceedings of the 2005 ACM symposium on Applied computing
Growing kernel-based self-organized maps trained with supervised bias
Intelligent Data Analysis
Symbolic adaptive neuro-fuzzy inference for data mining of heterogenous data
Intelligent Data Analysis
ICCOMP'05 Proceedings of the 9th WSEAS International Conference on Computers
Shape recognition for Irish sign language understanding
SMO'09 Proceedings of the 9th WSEAS international conference on Simulation, modelling and optimization
Dimensionality reduction oriented toward the feature visualization for ischemia detection
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
A multiobjective simultaneous learning framework for clustering and classification
IEEE Transactions on Neural Networks
Emerging patterns based methodology for prediction of patients with myocardial ischemia
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Non-rigid shape recognition for sign language understanding
WSEAS TRANSACTIONS on SYSTEMS
Clustering with kernel-based self-organized maps trained with supervised bias
SIP'06 Proceedings of the 5th WSEAS international conference on Signal processing
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The problem of maximizing the performance of the detection of ischemia episodes is a difficult pattern classification problem. The motivation for developing the supervising network self-organizing map (sNet-SOM) model is to exploit this fact for designing computationally effective solutions both for the particular ischemic detection problem and for other applications that share similar characteristics. Specifically, the sNet-SOM utilizes unsupervised learning for the “simple” regions 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 reduces to a size manageable numerically with a capable supervised model. The second learning phase has the objective of constructing better decision boundaries at the ambiguous regions. At this phase, a special supervised network is trained for the computationally reduced task of performing the classification at the ambiguous regions only. The utilization of sNet-SOM with supervised learning based on the radial basis functions and support vector machines has resulted in an improved accuracy of ischemia detection especially in the last case. The highly disciplined design of the generalization performance of the support vector machine allows designing the proper model for the number of patterns transferred to the supervised expert