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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.

  • 3 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

Firewall traversal method by inserting pseudo TCP header into QUIC

Taga, K., Zheng, J., Mouri, K., Saito, S. & Takimoto, E., Jan 1 2019, In : Lecture Notes in Engineering and Computer Science. 2239, p. 216-221 6 p.

Research output: Contribution to journalConference article

Network protocols
Internet
Communication
Industry
Linux

Implementation of virtual machine monitor-based stack trace mechanism on Windows 10 x64

Yamashita, Y., Zheng, J., Saito, S., Takimoto, E. & Mouri, K., Jan 1 2019, In : Lecture Notes in Engineering and Computer Science. 2239, p. 100-105 6 p.

Research output: Contribution to journalConference article

Visualization
Virtual machine
Malware

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
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

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