Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Competitive learning algorithms for vector quantization
Neural Networks
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Self-Organizing Maps
Applied Intelligence
The Supervised Network Self-Organizing Map for Classification of Large Data Sets
Applied Intelligence
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Neighborhood Preservation in Nonlinear Projection Methods: An Experimental Study
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Analyses on the Generalised Lotto-Type Competitive Learning
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A Multilingual Text Mining Approach Based on Self-Organizing Maps
Applied Intelligence
Analysis and visualization of gene expression data using self-organizing maps
Neural Networks - New developments in self-organizing maps
An introduction to variable and feature selection
The Journal of Machine Learning Research
Variable selection using svm based criteria
The Journal of Machine Learning Research
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
Information-Theoretic Competitive Learning with Inverse Euclidean Distance Output Units
Neural Processing Letters
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Lower and Upper Bounds for Misclassification Probability Based on Renyi's Information
Journal of VLSI Signal Processing Systems
Semi-Supervised Self-Training of Object Detection Models
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Feature Discovery by Enhancement and Relaxation of Competitive Units
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Enhancing and Relaxing Competitive Units for Feature Discovery
Neural Processing Letters
Self-enhancement learning: self-supervised and target-creating learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Free energy-based competitive learning for self-organizing maps
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
Applied Intelligence
Visualization of topology representing networks
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Self-organizing maps, vector quantization, and mixture modeling
IEEE Transactions on Neural Networks
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Feature selection in MLPs and SVMs based on maximum output information
IEEE Transactions on Neural Networks
The parameterless self-organizing map algorithm
IEEE Transactions on Neural Networks
Cooperative information maximization with Gaussian activation functions for self-organizing maps
IEEE Transactions on Neural Networks
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
Rival penalized competitive learning for clustering analysis, RBF net, and curve detection
IEEE Transactions on Neural Networks
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
In this paper, we propose a new type of information-theoretic method called "double enhancement learning," in which two types of enhancement, namely, self-enhancement and information enhancement, are unified. Self-enhancement learning has been developed to create targets spontaneously within a network, and its performance has proven to be comparable with that of conventional competitive learning and self-organizing maps. To improve the performance of the self-enhancement learning, we try to include information on input variables in the framework of self-enhancement learning. The information on input variables is computed by information enhancement in which a specific input variable is used to enhance competitive unit outputs. This information is again used to train a network with the self-enhancement learning. We applied the method to three problems, namely, an artificial data, a student survey and the voting attitude problem. In all three problems, quantization errors were significantly decreased with the double enhancement learning. The topographic errors were relatively higher, but the smallest number of topographic errors was also obtained by the double enhancement learning. In addition, we saw that U-matrices for all problems showed explicit boundaries reflecting the importance of input variables.