Title :
Clustering with mean field annealing and unsupervised learning
Author_Institution :
Dept. of Appl. Math., Zhejiang Univ., Hangzhou, China
Abstract :
When neural networks are used to solve a clustering problem, there is often no precise measure. However, in such fields as pattern recognition, a clustering problem is often with an objective function. In this paper, mean field theory neural nets are taken to tackle such a problem. Even when the number of clusters is unknown, an unsupervised neural network with gradient descent can evaluate it. The experimental result is satisfactory.<>
Keywords :
neural nets; pattern recognition; simulated annealing; unsupervised learning; clustering problem; gradient descent; mean field annealing; mean field theory neural nets; neural networks; objective function; pattern recognition; unsupervised learning; unsupervised neural network; Annealing; Costs; Equations; Neurons; Temperature; Unsupervised learning;
Conference_Titel :
TENCON '93. Proceedings. Computer, Communication, Control and Power Engineering.1993 IEEE Region 10 Conference on
Conference_Location :
Beijing, China
Print_ISBN :
0-7803-1233-3
DOI :
10.1109/TENCON.1993.320128
