Vector quantization and signal compression
Vector quantization and signal compression
Self-organizing maps
On-line learning and stochastic approximations
On-line learning in neural networks
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Evolutionary Design of Nearest Prototype Classifiers
Journal of Heuristics
Nearest prototype classification of noisy data
Artificial Intelligence Review
Model fusion-based batch learning with application to oil spills detection
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
| Hi-index | 0.00 |
We introduce a batch learning algorithm to design the set of prototypes of 1 nearest-neighbour classifiers. Like Kohonen's LVQ algorithms, this procedure tends to perform vector quantization over a probability density function that has zero points at Bayes borders. Although it differs significantly from their online counterparts since: (1) its statistical goal is clearer and better defined; and (2) it converges superlinearly due to its use of the very fast Newton's optimization method. Experiments results using artificial data confirm faster training time and better classification performance than Kohonen's LVQ algorithms.