Nobuhiro Yugami

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A constraint satisfaction problem (CSP) is a problem to find an assignment that satisfies given constraints. An interesting approach to CSP is a repair-based method that first generates an initial assignment, then repairs it by minimizing the number of conflicts. Min-conflicts hill climbing (MCHC) and GSAT are typical examples of this approach. A serious(More)
This paper presents average-case analyses of instance-based learning algorithms. The algorithms analyzed employ a variant of k-nearest neighbor classiÿer (k-NN). Our analysis deals with a monotone m-of-n target concept with irrelevant attributes, and handles three types of noise: relevant attribute noise, irrelevant attribute noise, and class noise. We(More)
This paper presents a statistical analysis of the Davis-Putnam procedure and propositional satisfiability problems (SAT). SAT has been researched in AI because of its strong relationship to automated reasoning and recently it is used as a benchmark problem of constraint satisfaction algorithms. The Davis-Putnam procedure is a well-known satisfiability(More)
This paper presents an average-case analysis of the fc-nearest neighbor classifier (k-NN). Our analysis deals with m-of-n// concepts, and handles three types of noise: relevant attribute noise, irrelevant attribute noise, and class noise. We formally compute the expected classification accuracy of fc-NN after a certain fixed number of training instances.(More)
Spreadsheets are widely used to manage statistical data, and a massive amount of valuable spreadsheets are now public as open data. Spreadsheet headers may contain hierarchical structures, and values in spreadsheets are associated with not only headers at the same column or row but also those corresponding to their ancestors. When integrating spread-sheets(More)