Title :
Combining Hard and Soft Competition in Information-Theoretic Learning
Author :
Kamimura, Ryotaro
Author_Institution :
Inf. Sci. Lab., Tokai Univ., Hiratsuka
Abstract :
In this paper, we try to combine conventional competitive learning with information-theoretic methods to improve competitive performance. We have so far proposed a new type of information-theoretic method to simulate competitive processes. Though the information-theoretic method solves the dead neuron problem and shows the soft-type competition, the method is sometime slow in convergence. To solve this problem, we combine standard learning with information-theoretic learning. By this combination, we can shorten a learning process considerably
Keywords :
information theory; unsupervised learning; competitive learning; convergence; dead neuron problem; hard competition; information-theoretic learning; soft competition; soft-type competition; Computational intelligence; Computational modeling; Computer architecture; Convergence; Information processing; Information science; Laboratories; Neurons;
Conference_Titel :
Foundations of Computational Intelligence, 2007. FOCI 2007. IEEE Symposium on
Conference_Location :
Honolulu, HI
Print_ISBN :
1-4244-0703-6
DOI :
10.1109/FOCI.2007.371530