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- Nobuhiro Yugami, Yuiko Ohta, Hirotaka Hara
- AAAI
- 1994

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)

In this paper, we propose a new framework for product configuration that integrates a constraint satisfaction problem with case-based reasoning (CBR), and this framework is applied to an on-line sales system. Given a user query, CBR first retrieves similar cases from the case base in which past successful configurations are stored. Then, it formalizes… (More)

- Seishi Okamoto, Nobuhiro Yugami
- ICML
- 2000

- Nobuhiro Yugami, Yuiko Ohta, Seishi Okamoto
- PAKDD
- 2000

- Seishi Okamoto, Nobuhiro Yugami
- Theor. Comput. Sci.
- 2003

This paper presents average-case analyses of instance-based learning algorithms. The algorithms analyzed employ a variant of k-nearest neighbor classi-er (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)

- Seishi Okamoto, Nobuhiro Yugami
- ICML
- 1996

- Nobuhiro Yugami
- IJCAI
- 1995

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 wellknown satisfiability… (More)

- Seishi Okamoto, Nobuhiro Yugami
- IJCAI
- 1997

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)

- Yukiko Yoshida, Yuiko Ohta, Kenichi Kobayashi, Nobuhiro Yugami
- Discovery Science
- 2003

- Seishi Okamoto, Nobuhiro Yugami
- ICCBR
- 1997