Titlebook: Discovery Science; Third International Setsuo Arikawa,Shinichi Morishita Conference proceedings 2000 Springer-Verlag Berlin Heidelberg 200

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Computationally Efficient Heuristics for If-Then Rule Extraction from Feed-Forward Neural NetworksWe also introduce a computationally efficient technique based on ordered-attributes. It reduces search space significantly and finds valid and general rules for single nodes in the networks. Empirical results are shown.

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Language Learning with a Neighbor Systemnsider is the one described uniformly in terms of a distance over strings. Firstly, we introduce a notion of a recursively generable distance over strings, and define a k-neighbor closure of a language L as the collection of strings each of which is at most k distant from some string in L. Then we d

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EXTOL

Knowledge Discovery from fMRI Brain Images by Logical Regression Analysistwo steps. The first step is regression analysis. The second step is rule extraction from the regression formula obtained by the regression analysis. In this paper, we use nonparametric regression analysis as a regression analysis, since there are not sufficient data in knowledge discovery from fMRI

Condyle

Human Discovery Processes Based on Searching Experiments in Virtual Psychological Research Environmes sometimes dificult to make complete experimental planning in which hypotheses guiding the experiments are established prior to executing the experiments. Even if the situation stands, experts in the field systematically organize their experimental processes. We propose Searching Experimental Schem

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Prediction of Binding Affinities for Protein-Ligand Complexes with Neural Network Modelsrmation of ligandtarget complexes is typical for biologically active compounds, including activators and inhibitors of various enzymes. Prediction of the dissociation constant (Kd) of protein-ligand complex is often used as a scoring function for the modeled complexes and there are many approaches i

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