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Author(s): Carpenter, G.A. | Kopco, N. |
Year: 2004
Citation: International Journal of Hybrid Intelligent Systems, 1(2), 57-71.
Abstract: A memory-based learning system called PointMap is introduced. The algorithm, an extension of Condensed Nearest Neighbor, evaluates the information value of coding nodes during training, and uses this index to prune non-informative nodes either on-line or after training. These pruning methods allow PointMap to control both code size and sensitivity to detail in the training data. Coding and pruning computations are local in space, with only the nearest coded neighbor available for comparison with the input; and in time, with only the current input available during coding. Pruning helps solve two problems of traditional memory-based learning systems: large memory requirements and sensitivity to noise. PointMap copes with the curse of dimensionality by considering multiple nearest neighbors during testing without increasing the complexity of the training process or the stored code. The performance of PointMap is compared to that of a group of sixteen nearest-neighbor systems on benchmark problems.
Topics:
Machine Learning,
Models:
Other,
