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Adaptive k-means algorithm with error-weighted deviation measure

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2 Author(s)
Chinrungrueng, C. ; Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA, USA ; Sequin, C.H.

The k-means algorithm can be used in multi-module networks to partition the input domain of supervised learning problems. The traditional k-means algorithm partitions the input domain based solely on the distribution of the input vectors. A modified algorithm is presented. It also integrates into its partitioning process information about the mismatch between the network function and the goal function. It uses an efficient adaptive learning rate and an error-weighted squared Euclidean distance measure that aims at equalizing the average approximation errors in all regions of the partition

Published in:

Neural Networks, 1993., IEEE International Conference on

Date of Conference:

1993