Multi-layered greedy network-growing algorithm: extension of greedy network-growing algorithm to multi-layered networks.
In this paper, we extend our greedy network-growing algorithm to multi-layered networks. With multi-layered networks, we can solve many complex problems that single-layered networks fail to solve. In addition, the network-growing algorithm is used in conjunction with teacher-directed learning that produces appropriate outputs without computing errors between targets and outputs. Thus, the present algorithm is a very efficient network-growing algorithm. The new algorithm was applied to three problems: the famous vertical-horizontal lines detection problem, a medical data problem and a road classification problem. In all these cases, experimental results confirmed that the method could solve problems that single-layered networks failed to. In addition, information maximization makes it possible to extract salient features in input patterns.
Kamimura
Ryotaro
R
Information Science Laboratory, Tokai University, 1117 Kitakaname Hiratsuka Kanagawa 259-1292, Japan. ryo@cc.u-tokai.ac.jp
D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology; D001330:Electronic Data Processing; D006439:Hemodynamics; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016571:Neural Networks, Computer; D011337:Probability Learning