Biomedical subjects

Ryotaro Kamimura

Publications and source records attributed to Ryotaro Kamimura.

3 recordsLinked to original sources

PubMed2006

Cooperative information maximization with Gaussian activation functions for self-organizing maps.

In this paper, we propose a new information-theoretic method to produce explicit self-organizing maps (SOMs). Competition is realized by maximizing mutual information between input patterns and competitive units. Competitive unit outputs are computed by the Gaussian function of distance between input patterns and competitive units. A property of this Gaussian function is that, as distance becomes smaller, a neuron tends to fire strongly. Cooperation processes are realized by taking into account the firing rates of neighboring neurons. We applied our method to uniform distribution learning, chemical compound classification and road classification. Experimental results confirmed that cooperation processes could significantly increase information content in input patterns. When cooperative operations are not effective in increasing information, mutual information as well as entropy maximization is used to increase information. Experimental results showed that entropy maximization could be used to increase information and to obtain clearer SOMs, because competitive units are forced to be equally used on average.

Cluster Analysis↗

PubMed2004

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.

Algorithms↗

PubMedDate not supplied

Unifying cost and information in information-theoretic competitive learning.

In this paper, we introduce costs into the framework of information maximization and try to maximize the ratio of information to its associated cost. We have shown that competitive learning is realized by maximizing mutual information between input patterns and competitive units. One shortcoming of the method is that maximizing information does not necessarily produce representations faithful to input patterns. Information maximizing primarily focuses on some parts of input patterns that are used to distinguish between patterns. Therefore, we introduce the cost, which represents average distance between input patterns and connection weights. By minimizing the cost, final connection weights reflect input patterns well. We applied the method to a political data analysis, a voting attitude problem and a Wisconsin cancer problem. Experimental results confirmed that, when the cost was introduced, representations faithful to input patterns were obtained. In addition, improved generalization performance was obtained within a relatively short learning time.

Algorithms↗