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Fingerprint Dive into the research topics where Eiji Takimoto is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

  • 5 Similar Profiles
Boolean functions Engineering & Materials Science
Threshold Circuits Mathematics
Decision trees Engineering & Materials Science
Boosting Mathematics
Decision tree Mathematics
Online Optimization Mathematics
Learning algorithms Engineering & Materials Science
Regret Mathematics

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Research Output 1993 2019

Recognition of Japanese historical hand-written characters based on object detection methods

Tang, Y., Hatano, K. & Takimoto, E., Sep 20 2019, Proceedings of the 2019 Workshop on Historical Document Imaging and Processing, HIP 2019. Association for Computing Machinery, p. 72-77 6 p. (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Labels
Classifiers
Object detection

Special section on discrete mathematics and its applications

Kawachi, A., Yamauchi, Y., Ito, T., Uchizawa, K., Otachi, Y., Ono, H., Kimura, K., Koga, H., Saitoh, T., Shibuya, T., Takimoto, E., Tanigawa, S. I., Nuida, K., Fukushima, K., Fujiwara, H., Mizuki, T., Moriyama, S. & Sadakane, K., Jan 1 2019, In : IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences. E102A, 9, 1 p.

Research output: Contribution to journalEditorial

Discrete mathematics

Succinct representation of linear extensions via MDDs and its application to scheduling under precedence constraints

Miyake, F., Takimoto, E. & hatano, K., Jan 1 2019, Combinatorial Algorithms - 30th International Workshop, IWOCA 2019, Proceedings. Colbourn, C. J., Grossi, R. & Pisanti, N. (eds.). Springer Verlag, p. 365-377 13 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11638 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Linear Extension
Precedence Constraints
Decision Diagrams
Scheduling
Flow Time

Boosting over non-deterministic ZDDs

Fujita, T., hatano, K. & Takimoto, E., Jan 1 2018, (Accepted/In press) In : Theoretical Computer Science.

Research output: Contribution to journalArticle

Binary decision diagrams
Boosting
Adaptive boosting
Decision Diagrams
Learning algorithms
1 Citation (Scopus)

Boosting over non-deterministic ZDDs

Fujita, T., hatano, K. & Takimoto, E., Jan 1 2018, WALCOM: Algorithms and Computation - 12th International Conference, WALCOM 2018, Proceedings. Rahman, M. S., Sung, W-K. & Uehara, R. (eds.). Springer Verlag, p. 195-206 12 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 10755 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Binary decision diagrams
Boosting
Adaptive boosting
Decision Diagrams
Learning algorithms