Autori: S. Papadimitriou, L. Vladutu, S. Mavroudi and A. Bezerianos

Editorial: IEEE, IEEE Transactions on Neural Networks, 12 issue 3, p.503-515, 2001.

Rezumat:

The problem of maximizing the performance of the detection of ischemia episodes is a difficult pattern classification problem. The state space for this problem is consisted of regions that lie near class separation boundaries and require the construction of complex discriminants while for the rest regions the classification task is significantly simpler. The motivation for developing the Network Self-Organizing Map (NetSOM) model is to exploit this fact for designing computationally effective solutions. Specifically, the NetSOM 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 second learning phase (the supervised training) 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 NetSOM 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.

Cuvinte cheie: Divide and conquer algorithms, entropy, ischemia, Principal Components Analysis, Support Vector Machines

URL: http://heart.med.upatras.gr/~liviu