Machine Learning
Rule Extraction from Prediction Models
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Knowledge Discovery from fMRI Brain Images by Logical Regression Analysis
DS '00 Proceedings of the Third International Conference on Discovery Science
Rule Discovery from fMRI Brain Images by Logical Regression Analysis
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
Multi-aspect data analysis in brain informatics
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Peculiarity oriented fMRI brain data analysis for studying human multi-perception mechanism
Cognitive Systems Research
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As a result of the ongoing development of non-invasive analysis of brain function, detailed brain images can be obtained, from which the relations between brain areas and brain functions can be understood. Researchers are trying to heuristically discover the relations between brain areas and brain functions from brain images. As the relations between brain areas and brain functions are described by rules, the discovery of relations between brain areas and brain functions from brain images is the discovery of rules from brain images. The discovery of rules from brain images is a discovery of rules from pattern data, which is a new field different from the discovery of rules from symbolic data or numerical data. This paper presents an algorithm for the discovery of rules from brain images. The algorithm consists of two steps. The first step is nonparametric regression. The second step is rule extraction from the linear formula obtained by the nonparametric regression. We have to confirm that the algorithm works well for artificial data before the algorithm is applied to real data. This paper shows that the algorithm works well for artificial data.