Distinction Sensitive Learning Vector Quantisation-a new noise-insensitive classification method
- 17 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 5, 2890-2894
- https://doi.org/10.1109/icnn.1994.374690
Abstract
A Distinction Sensitive Learning Vector Quantizer (DSLVQ), based on the LVQ3 algorithm, is introduced which automatically adjusts the influence of the input features according to their observed relevance for classification. DSLVQ is less sensitive to noisy features than standard LVQ and its importance adjustments are transparent and can be exploited for input data feature selection. As an example, the algorithm is applied to the classification of two artificial data sets: Breiman's (1984) waveform data and Kohonen's "hard" classification task.<>Keywords
This publication has 5 references indexed in Scilit:
- Automated feature selection with a distinction sensitive learning vector quantizerNeurocomputing, 1996
- A weighted nearest neighbor algorithm for learning with symbolic featuresMachine Learning, 1993
- Instance-based learning algorithmsMachine Learning, 1991
- The self-organizing mapProceedings of the IEEE, 1990
- Statistical pattern recognition with neural networks: benchmarking studiesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1988