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Atsuyoshi Nakamura
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Publications
- 2020
- [c50]Mitsuki Maekawa, Atsuyoshi Nakamura, Mineichi Kudo:
Data-Dependent Conversion to a Compact Integer-Weighted Representation of a Weighted Voting Classifier. ACML 2020: 241-256 - 2018
- [j20]Ryo Watanabe, Junpei Komiyama, Atsuyoshi Nakamura, Mineichi Kudo:
UCB-SC: A Fast Variant of KL-UCB-SC for Budgeted Multi-Armed Bandit Problem. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 101-A(3): 662-667 (2018) - 2017
- [j19]Koji Tabata, Atsuyoshi Nakamura, Mineichi Kudo:
An Efficient Approximate Algorithm for the 1-Median Problem on a Graph. IEICE Trans. Inf. Syst. 100-D(5): 994-1002 (2017) - [j18]Ryo Watanabe, Junpei Komiyama, Atsuyoshi Nakamura, Mineichi Kudo:
KL-UCB-Based Policy for Budgeted Multi-Armed Bandits with Stochastic Action Costs. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 100-A(11): 2470-2486 (2017) - [i2]Hiroyuki Hanada, Mineichi Kudo, Atsuyoshi Nakamura:
On Practical Accuracy of Edit Distance Approximation Algorithms. CoRR abs/1701.06134 (2017) - 2016
- [j17]Atsuyoshi Nakamura, Ichigaku Takigawa, Hisashi Tosaka, Mineichi Kudo, Hiroshi Mamitsuka:
Mining approximate patterns with frequent locally optimal occurrences. Discret. Appl. Math. 200: 123-152 (2016) - [c44]Shunsuke Suzuki, Mineichi Kudo, Atsuyoshi Nakamura:
Sitting posture diagnosis using a pressure sensor mat. ISBA 2016: 1-6 - 2015
- [j16]Ryo Watanabe, Atsuyoshi Nakamura, Mineichi Kudo:
An improved upper bound on the expected regret of UCB-type policies for a matching-selection bandit problem. Oper. Res. Lett. 43(6): 558-563 (2015) - [c42]Koji Tabata, Atsuyoshi Nakamura, Mineichi Kudo:
An Algorithm for Influence Maximization in a Two-Terminal Series Parallel Graph and its Application to a Real Network. Discovery Science 2015: 275-283 - [c41]Ayako Mikami, Mineichi Kudo, Atsuyoshi Nakamura:
Diversity Measures and Margin Criteria in Multi-class Majority Vote Ensemble. MCS 2015: 27-37 - 2014
- [j15]Koji Ouchi, Atsuyoshi Nakamura, Mineichi Kudo:
An efficient construction and application usefulness of rectangle greedy covers. Pattern Recognit. 47(3): 1459-1468 (2014) - [j14]Hiroyuki Hanada, Mineichi Kudo, Atsuyoshi Nakamura:
Average-case linear-time similar substring searching by the q-gram distance. Theor. Comput. Sci. 530: 23-41 (2014) - 2013
- [j13]Atsuyoshi Nakamura, Tomoya Saito, Ichigaku Takigawa, Mineichi Kudo, Hiroshi Mamitsuka:
Fast algorithms for finding a minimum repetition representation of strings and trees. Discret. Appl. Math. 161(10-11): 1556-1575 (2013) - 2012
- [c39]Koji Tabata, Atsuyoshi Nakamura, Mineichi Kudo:
Fast Approximation Algorithm for the 1-Median Problem. Discovery Science 2012: 169-183 - [c38]Atsuyoshi Nakamura, Hisashi Tosaka, Mineichi Kudo:
Frequent Approximate Substring Pattern Mining Using Locally Optimal Occurrence Counting. IIAI-AAI 2012: 54-59 - 2011
- [j11]Tetsuji Takahashi, Mineichi Kudo, Atsuyoshi Nakamura:
Construction of convex hull classifiers in high dimensions. Pattern Recognit. Lett. 32(16): 2224-2230 (2011) - [c37]Hiroyuki Hanada, Atsuyoshi Nakamura, Mineichi Kudo:
A practical comparison of edit distance approximation algorithms. GrC 2011: 231-236 - [c36]Koji Ouchi, Atsuyoshi Nakamura, Mineichi Kudo:
Efficient construction and usefulness of hyper-rectangle greedy covers. GrC 2011: 533-538 - [c35]Atsuyoshi Nakamura, Mineichi Kudo:
Packing Alignment: Alignment for Sequences of Various Length Events. PAKDD (2) 2011: 234-245 - 2010
- [c34]Taishi Uchiya, Atsuyoshi Nakamura, Mineichi Kudo:
Algorithms for Adversarial Bandit Problems with Multiple Plays. ALT 2010: 375-389 - [c33]Atsuyoshi Nakamura, Tomoya Saito, Ichigaku Takigawa, Hiroshi Mamitsuka, Mineichi Kudo:
Algorithms for Finding a Minimum Repetition Representation of a String. SPIRE 2010: 185-190 - 2009
- [j10]Ichigaku Takigawa, Mineichi Kudo, Atsuyoshi Nakamura:
Convex sets as prototypes for classifying patterns. Eng. Appl. Artif. Intell. 22(1): 101-108 (2009) - [c32]Tetsuji Takahashi, Mineichi Kudo, Atsuyoshi Nakamura:
Classifier Selection in a Family of Polyhedron Classifiers. CIARP 2009: 441-448 - [c31]Satoshi Shirai, Mineichi Kudo, Atsuyoshi Nakamura:
Comparison of Bagging and Boosting Algorithms on Sample and Feature Weighting. MCS 2009: 22-31 - 2008
- [j9]Yohji Shidara, Mineichi Kudo, Atsuyoshi Nakamura:
Classification Based on Consistent Itemset Rules. Trans. Mach. Learn. Data Min. 1(1): 17-30 (2008) - [c30]Atsuyoshi Nakamura, Mineichi Kudo:
What Sperner Family Concept Class is Easy to Be Enumerated? ICDM 2008: 482-491 - [c29]Mineichi Kudo, Atsuyoshi Nakamura, Ichigaku Takigawa:
Classification by reflective convex hulls. ICPR 2008: 1-4 - [c28]Yohji Shidara, Mineichi Kudo, Atsuyoshi Nakamura:
Classification by bagged consistent itemset rules. ICPR 2008: 1-4 - [c27]Satoshi Shirai, Mineichi Kudo, Atsuyoshi Nakamura:
Bagging, Random Subspace Method and Biding. SSPR/SPR 2008: 801-810 - 2007
- [c26]Hisashi Tosaka, Atsuyoshi Nakamura, Mineichi Kudo:
Mining Subtrees with Frequent Occurrence of Similar Subtrees. Discovery Science 2007: 286-290 - [c25]Yohji Shidara, Atsuyoshi Nakamura, Mineichi Kudo:
CCIC: Consistent Common Itemsets Classifier. MLDM 2007: 490-498 - 2005
- [c23]Mineichi Kudo, Atsuyoshi Nakamura:
Specific-Purpose Web Searches on the Basis of Structure and Contents. Federation over the Web 2005: 79-96 - [c22]Hidehiko Ino, Mineichi Kudo, Atsuyoshi Nakamura:
A Comparative Study of Algorithms for Finding Web Communities. ICDE Workshops 2005: 1257 - [c21]Hiroyuki Hasegawa, Mineichi Kudo, Atsuyoshi Nakamura:
Empirical Study on Usefulness of Algorithm SACwRApper for Reputation Extraction from the WWW. KES (4) 2005: 668-674 - [c20]Ichigaku Takigawa, Mineichi Kudo, Atsuyoshi Nakamura:
The Convex Subclass Method: Combinatorial Classifier Based on a Family of Convex Sets. MLDM 2005: 90-99 - [c19]Atsuyoshi Nakamura, Mineichi Kudo:
Mining Frequent Trees with Node-Inclusion Constraints. PAKDD 2005: 850-860 - [c18]Hidehiko Ino, Mineichi Kudo, Atsuyoshi Nakamura:
Partitioning of Web graphs by community topology. WWW 2005: 661-669 - [p1]Yohji Shidara, Mineichi Kudo, Atsuyoshi Nakamura:
Extraction of Generalized Rules with Automated Attribute Abstraction. Foundations of Data Mining and knowledge Discovery 2005: 161-170 - 2004
- [c16]Ichigaku Takigawa, Mineichi Kudo, Atsuyoshi Nakamura, Jun Toyama:
On the Minimum l1-Norm Signal Recovery in Underdetermined Source Separation. ICA 2004: 193-200 - 2003
- [c15]Atsuyoshi Nakamura, Mineichi Kudo, Akira Tanaka, Kazuhiko Tanabe:
Collaborative Filtering Using Projective Restoration Operators. Discovery Science 2003: 393-401 - [c14]Atsuyoshi Nakamura, Mineichi Kudo, Akira Tanaka:
Collaborative Filtering Using Restoration Operators. PKDD 2003: 339-349
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