Neural computation and self-organizing maps: an introduction
Neural computation and self-organizing maps: an introduction
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Neural networks for pattern recognition
Neural networks for pattern recognition
Fuzzy engineering
Data mining and knowledge discovery Internet resources
Advances in knowledge discovery and data mining
Computers and Biomedical Research
An equivalence between sparse approximation and support vector machines
Neural Computation
Three remarks on the support vector method of function estimation
Advances in kernel methods
Generalization performance of support vector machines and other pattern classifiers
Advances in kernel methods
Making large-scale support vector machine learning practical
Advances in kernel methods
Independent component analysis: theory and applications
Independent component analysis: theory and applications
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Symbolical Reasoning about Numerical Data: A Hybrid Approach
Applied Intelligence
Applied Intelligence
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Artificial neural networks for automatic ECG analysis
IEEE Transactions on Signal Processing
ECG analysis using nonlinear PCA neural networks for ischemiadetection
IEEE Transactions on Signal Processing
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Successive overrelaxation for support vector machines
IEEE Transactions on Neural Networks
Model complexity control for regression using VC generalization bounds
IEEE Transactions on Neural Networks
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
Analysis of input-output clustering for determining centers of RBFN
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Complex application domains involve difficult pattern classification problems. The state space of these problems consists 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 Supervised Network Self-Organizing Map (SNet-SOM) model is to exploit this fact for designing computationally effective solutions. Specifically, the SNet-SOM utilizes unsupervised learning for classifying at the simple regions and supervised learning for the difficult ones in a two stage learning process. The unsupervised learning approach is based on the Self-Organizing Map (SOM) of Kohonen. The basic SOM is modified with a dynamic node insertion/deletion process controlled with an entropy based criterion that allows an adaptive extension of the SOM. This extension proceeds until the total number of training patterns that are mapped to neurons with high entropy (and therefore with ambiguous classification) reduces to a size manageable numerically with a capable supervised model. 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 performance of the SNet-SOM has been evaluated on both synthetic data and on an ischemia detection application with data extracted from the European ST-T database. In all cases, the utilization of SNet-SOM with supervised learning based on both Radial Basis Functions and Support Vector Machines has improved the results significantly related to those obtained with the unsupervised SOM and has enhanced the scalability of the supervised learning schemes. The highly disciplined design of the generalization performance of the Support Vector Machine allows to design the proper model for the particular training set.