Title :
Unsupervised learning with the soft-means algorithm
Author_Institution :
Sch. of Cognitive & Comput. Sci., Sussex Univ., Brighton, UK
fDate :
27 Jun-2 Jul 1994
Abstract :
This note describes a useful adaptation of the `peak seeking´ regime used in unsupervised learning processes such as competitive learning and `k-means´. The adaptation enables the learning to capture low-order probability effects and thus to more fully capture the probabilistic structure of the training data
Keywords :
neural nets; unsupervised learning; competitive learning; k-means learning; low-order probability effects; neural nets; peak seeking; probabilistic structure; soft-means algorithm; unsupervised learning; Data compression; Input variables; Iterative methods; Machine learning; Neural networks; Probability; Statistics; Training data; Unsupervised learning;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374742
