DocumentCode
288786
Title
Unsupervised learning with the soft-means algorithm
Author
Thornton, Chris
Author_Institution
Sch. of Cognitive & Comput. Sci., Sussex Univ., Brighton, UK
Volume
5
fYear
1994
fDate
27 Jun-2 Jul 1994
Firstpage
3173
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;
fLanguage
English
Publisher
ieee
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
Type
conf
DOI
10.1109/ICNN.1994.374742
Filename
374742
Link To Document