Kohei Hatano | Last modified date：2023.04.10 |

Professor /
Department of Informatics /
Faculty of Information Science and Electrical Engineering

**Papers**

1. | Sherief Hashima, Mostafa M. Fouda, Zubair Md Fadlullah, Ehab Mahmoud Mohamed, Kohei Hatano, Improved UCB-based Energy-Efficient Channel Selection in Hybrid-Band Wireless Communication, Proceedings of GLOBECOM '21, 10.1109/GLOBECOM46510.2021.9685996, 2021.12. |

2. | Yaxiong Liu, Ken-ichiro Moridomi, Kohei Hatano, Eiji Takimoto, An online semi-definite programming with a generalised log-determinant regularizer and its applications, Proceedings of The 13th Asian Conference on Machine Learning (ACML), PMLR 157, 1113-1128, 2021.11. |

3. | Yaxiong Liu, Xuanke Jiang, Kohei Hatano, Eiji Takimoto, Expert advice problem with noisy low rank loss, Proceedings of The 13th Asian Conference on Machine Learning (ACML), PMLR 157, 1097-1112, 2021.11. |

4. | Ehab Mahmoud Mohamed, Sherief Hashima, Kohei Hatano, Mostafa M. Fouda, Zubair Md. Fadlullah, Sleeping Contextual/Non-Contextual Thompson Sampling MAB for mmWave D2D Two-Hop Relay Probing., IEEE Transactions on Vehicular Technology, 10.1109/TVT.2021.3116223, 70, 11, 12101-12112, 2021.11. |

5. | Sherief Hashima, Ehab Mahmoud Mohamed, Kohei Hatano, Eiji Takimoto, WiGig Wireless Sensor Selection Using Sophisticated Multi Armed Bandit Schemes, 2021 13th International Conference on Mobile Computing and Ubiquitous Network (ICMU), 2021.11. |

6. | Yiping Tang, Kohei Hatano, Eiji Takimoto, Recognition of Japanese historical hand-written characters based on object detection methods, Proceedings of the 5th International Workshop on Historical Document Imaging and Processing(HIP2019) , 10.1145/3352631.3352642, 72-77, 2019.09, We consider the recognition problem of Japanese historical handwritten characters called “Kuzushiji”. Unlike modern characters, Kuzushiji characters are harder to recognize partly because many of them are connected and not separated by spaces without any segmentation information. We propose two methods for segmentation and recognition of Kuzushiji characters. The first method learns segmentation rules and character classifiers simultaneously from data sets with character labels and segmentation information. Second method is for segmentation and can be used with any single character recognizer. Our methods outperform other baselines and achieve the state-of-the-art accuracy on both segmentation and recognition tasks on data sets of three consecutive Kuzushiji characters.. |

7. | Kaori Tamura, Min Lu, Shin’ichi Konomi, Kohei Hatano, Miyuki Inaba, Misato Oi, Tsuyoshi Okamoto, Fumiya Okubo, Atsushi Shimada, Jingyun Wang, Masanori Yamada, Yuki Yamada, Integrating Multimodal Learning Analytics and Inclusive Learning Support Systems for People of All Ages, Proceedings of the11th International Conference on Cross-Cultural Design(CCD2019), 10.1007/978-3-030-22580-3_35, 469-481, 2019.07, Extended learning environments involving system to collect data for learning analytics and to support learners will be useful for all-age education. As the first steps towards to build new learning environments, we developed a system for multimodal learning analytics using eye-tracker and EEG measurement, and inclusive user interface design for elderly learners by dual-tablet system. Multimodal learning analytics system can be supportive to extract where and how learners with varied backgrounds feel difficulty in learning process. The eye-tracker can retrieve information where the learners paid attention. EEG signals will provide clues to estimate their mental states during gazes in learning. We developed simultaneous measurement system of these multimodal responses and are trying to integrate the information to explore learning problems. A dual-tablet user interface with simplified visual layers and more intuitive operations was designed aiming to reduce the physical and mental loads of elderly learners. A prototype was developed based on a cross-platform framework, which is being refined by iterative formative evaluations participated by elderlies, in order to improve the usability of the interface design. We propose a system architecture applying the multimodal learning analytics and the user-friendly design for elderly learners, which couples learning analytics “in the wild” environment and learning analytics in controlled lab environments.. |

8. | Fumito Miyake, Eiji Takimoto, kohei hatano, Succinct representation of linear extensions via MDDs and its application to scheduling under precedence constraints, 30th International Workshop on Combinatorial Algorithms, IWOCA 2019
Combinatorial Algorithms - 30th International Workshop, IWOCA 2019, Proceedings, 10.1007/978-3-030-25005-8_30, 365-377, 2019.07, We consider a single machine scheduling problem to minimize total flow time under precedence constraints, which is NP-hard. Matsumoto et al. proposed an exact algorithm that consists of two phases: first construct a Multi-valued Decision Diagram (MDD) to represent feasible permutations of jobs, and then find the shortest path in the MDD which corresponds to the optimal solution. Although their algorithm performs significantly better than standard IP solvers for problems with dense constraints, the performance rapidly diminishes when the number of constraints decreases, which is due to the exponential growth of MDDs. In this paper, we introduce an equivalence relation among feasible permutations and show that it suffices to construct an MDD that maintains only one representative for each equivalence class. Experimental results show that our algorithm outperforms Matsumoto et al.’s algorithm for problems with sparse constraints, while keeping good performance for dense constraints. Moreover, we show that Matsumoto et al.’s algorithm can be extended for solving a more general problem of minimizing weighted total flow time.. |

9. | Kaori Tamura, Tsuyoshi Okamoto, Misato Oi, Atsushi Shimada, Kohei Hatano, Masanori Yamada, Min Lu, Shin'ichi Konomi, Pilot study to estimate “difficult” area in e-learning material by physiological measurements, Proceedings of the 6th 2019 ACM Conference on Learning at Scale(L@S2019), 10.1145/3330430.3333648, 2019.06, To improve designs of e-learning materials, it is necessary to know which word or figure a learner felt "difficult" in the materials. In this pilot study, we measured electroencephalography (EEG) and eye gaze data of learners and analyzed to estimate which area they had difficulty to learn. The developed system realized simultaneous measurements of physiological data and subjective evaluations during learning. Using this system, we observed specific EEG activity in difficult pages. Integrating of eye gaze and EEG measurements raised a possibility to determine where a learner felt “difficult” in a page of learning materials. From these results, we could suggest that the multimodal measurements of EEG and eye gaze would lead to effective improvement of learning materials. For future study, more data collection using various materials and learners with different backgrounds is necessary. This study could lead to establishing a method to improve e-learning materials based on learners' mental states.. |

10. | Min Lu, Kaori Tamura, Tsuyoshi Okamoto, Misato Oi, Atsushi Shimada, Kohei Hatano, Masanori Yamada, Shin'ichi Konomi, Proposal and implementation of an elderly-oriented user interface for learning support systems, Proceedings of the 6th 2019 ACM Conference on Learning at Scale(L@S2019), 10.1145/3330430.3333650, 2019.06, Extended learning support systems for all-age education requires inclusive user interface design, especially for elderly users. A dual-tablet user interface with simplified visual layers and more intuitive operations was proposed aiming to reduce the physical and mental loads of elderly learners. An initial prototype with basic functions of viewing learning material was developed based on a cross-platform framework. Two preliminary user experiments participated by elderly volunteers were carried out for formative evaluations, in order to improve the usability of the interface design iteratively. The prototype was modified based on the participants’ comments and observation of their operations during the experiments. Additional findings of the elderly users’ preference and tendency were discussed for further development.. |

11. | Kohei Hatano, Combinatorial Online Prediction, 15th International Symposium on Information Theory and Its Applications, ISITA 2018
Proceedings of 2018 International Symposium on Information Theory and Its Applications, ISITA 2018, 10.23919/ISITA.2018.8664224, 40-44, 2019.03, We present a short survey on recent results on combinatorial online prediction in the adversarial setting.. |

12. | Takahiro Fujita, Kohei Hatano, Eiji Takimoto, Boosting over non-deterministic ZDDs, Theoretical Computer Science, https://doi.org/10.1016/j.tcs.2018.11.027, 2018.12, [URL], 本論文では，ZDDと呼ばれる圧縮データ構造を用いてデータを圧縮した上で，圧縮したデータ上でブースティングという機械学習手法が効率よく計算できる事を明らかにした．ビッグデータの解析において，省スペースで計算が出来ることは大きな利点である．本研究の興味深い点は，組合せ論的オンライン予測という（一見）全く異なる機械学習分野の知見が圧縮情報処理に活かされたことである．. |

13. | Takahiro Fujita, Kohei Hatano, and Eiji Takimoto, “, Online Combinatorial Optimization with Multiple Projections and Its Application to Scheduling Problem Volume and Number: Vol.,pp.-,Sep. 2018., IEICE Transactions on Information and Systems, E101-A, 9, 2018.09. |

14. | Takahiro Fujita, Kohei Hatano, Shuji Kijima, Eiji Takimoto, Online combinatorial optimization with multiple projections and its application to scheduling problem, IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 10.1587/transfun.E101.A.1334, E101A, 9, 1334-1343, 2018.09, We consider combinatorial online prediction problems and propose a new construction method of efficient algorithms for the problems. One of the previous approaches to the problem is to apply online prediction method, in which two external procedures the projection and the metarounding are assumed to be implemented. In this work, we generalize the projection to multiple projections. As an application of our framework, we show an algorithm for an online job scheduling problem with a single machine with precedence constraints.. |

15. | Ken-ichiro Moridomi, Kohei Hatano, Eiji Takimoto, Tighter generalization bounds for matrix completion via factorization into constrained matrices, IEICE Transactions on Information and Systems, 10.1587/transinf.2017EDP7339, E101D, 8, 1997-2004, 2018.08, We prove generalization error bounds of classes of low-rank matrices with some norm constraints for collaborative filtering tasks. Our bounds are tighter, compared to known bounds using rank or the related quantity only, by taking the additional L_{1} and L_{∞} constraints into account. Also, we show that our bounds on the Rademacher complexity of the classes are optimal.. |

16. | Ken-ichiro Moridomi, Kohei Hatano, and Eiji Takimoto, “, Online linear optimization with the log-determinant regularizer, IEICE Transactions on Information and Systems, E101-D, 6, 2018.06. |

17. | Kosuke Matsumoto, Kohei Hatano, Eiji Takimoto, Decision diagrams for solving a job scheduling problem under precedence constraints, 17th Symposium on Experimental Algorithms, SEA 2018
, 10.4230/LIPIcs.SEA.2018.5, 2018.06, We consider a job scheduling problem under precedence constraints, a classical problem for a single processor and multiple jobs to be done. The goal is, given processing time of n fixed jobs and precedence constraints over jobs, to find a permutation of n jobs that minimizes the total flow time, i.e., the sum of total wait time and processing times of all jobs, while satisfying the precedence constraints. The problem is an integer program and is NP-hard in general. We propose a decision diagram π-MDD, for solving the scheduling problem exactly. Our diagram is suitable for solving linear optimization over permutations with precedence constraints. We show the e ectiveness of our approach on the experiments on large scale artificial scheduling problems.. |

18. | Shinichi Konomi, Kohei Hatano, Miyuki Inaba, Misato Oi, Tsuyoshi Okamoto, Fumiya Okubo, Atsushi Shimada, Jingyun Wang, Masanori Yamada, Yuki Yamada, Extending Learning Analytics Platforms to Support Elderly People, The 12th International Workshop on Information Search, Integration, and Personalization (ISIP2018), 2018.05. |

19. | Daiki Suehiro, Kohei hatano, Eiji Takimoto, Efficient reformulation of 1-norm ranking SVM, IEICE Transactions on Information and Systems, 10.1587/transinf.2017EDP7233, E101D, 3, 719-729, 2018.03. |

20. | Takahiro Fujita, Kohei Hatano, Eiji Takimoto, Boosting over non-deterministic ZDDs, Proceedings of the 12th International Conference and Workshop on Algorithms and Computation( WALCOM 2018), 10.1007/978-3-319-75172-6_17, 195-206, 2018.01, 本論文では，ZDDと呼ばれる圧縮データ構造を用いてデータを圧縮した上で，圧縮したデータ上でブースティングという機械学習手法が効率よく計算できる事を明らかにした．ビッグデータの解析において，省スペースで計算が出来ることは大きな利点である．本研究の興味深い点は，組合せ論的オンライン予測という（一見）全く異なる機械学習分野の知見が圧縮情報処理に活かされたことである．. |

21. | Kohei Hatano, Can machine learning techniques provide better learning support for elderly people?, 6th International Conference on Distributed, Ambient and Pervasive Interactions, DAPI 2018 Held as Part of HCI International 2018
Distributed, Ambient and Pervasive Interactions
Technologies and Contexts - 6th International Conference, DAPI 2018, Held as Part of HCI International 2018, Proceedings, 10.1007/978-3-319-91131-1_14, 178-187, 2018.01, Computer-based support for learning of elderly people is now considered as an important issue in the super-aged society. Extra cares are needed for elderly people’s learning compared to younger people, since they might have difficulty in using computers, reduced cognitive ability and other physical problems which make them less motivated. Key components of a better learning support system are sensing the contexts surrounding elderly people and providing appropriate feedbacks to them. In this paper, we review some existing techniques of the contextual bandit framework in the machine learning literature, which could be potentially useful for online decision making scenarios given contexts. We also discuss issues and challenges to apply the framework.. |

22. | Shinichi Konomi, Kohei Hatano, Miyuki Inaba, Misato Terai, Tsuyoshi Okamoto, Fumiya Okubo, Atsushi Shimada, Jingyun Wang, Masanori Yamada, Yuki Yamada, Towards supporting multigenerational co-creation and social activities Extending learning analytics platforms and beyond, 6th International Conference on Distributed, Ambient and Pervasive Interactions, DAPI 2018 Held as Part of HCI International 2018
Distributed, Ambient and Pervasive Interactions
Technologies and Contexts - 6th International Conference, DAPI 2018, Held as Part of HCI International 2018, Proceedings, 10.1007/978-3-319-91131-1_6, 82-91, 2018.01, As smart technologies pervade our everyday environments, they change what people should learn to live meaningfully as valuable participants of our society. For instance, ubiquitous availability of smart devices and communication networks may have reduced the burden for people to remember factual information. At the same time, they may have increased the benefits to master the uses of new digital technologies. In the midst of such a social and technological shift, we could design novel integrated platforms that support people at all ages to learn, work, collaborate, and co-create easily. In this paper, we discuss our ideas and first steps towards building an extended learning analytics platform that elderly people and unskilled adults can use. By understanding the characteristics and needs of elderly learners and addressing critical user interface issues, we can build pervasive and inclusive learning analytics platforms that trigger contextual reminders to support people at all ages to live and learn actively regardless of age-related differences of cognitive capabilities. We discuss that resolving critical usability problems for elderly people could open up a plethora of opportunities for them to search and exploit vast amount of information to achieve various goals.. |

23. | Emi Ishita, Tetsuya Nakatoh, Kohei Hatano, Michiaki TAKAYAMA, An Attempt to Promote Open Data for Digital Humanities in Japanese University Libraries, Proceedings of the 18th International Conference on Asia-Pacific Digital Libraries (ICADL 2016), 10.1007/978-3-319-49304-6_32, LNCS 10075, 269-274, 2016.12. |

24. | Nir Ailon, Kohei Hatano, Eiji Takimoto, Bandit Online Optimization Over the Permutahedron, Theoretical Computer Science, 10.1016/j.tcs.2016.07.033, 650, 18, 92-108, 2016.10. |

25. | Takumi Nakazono, Ken-ichiro Moridomi, Kohei Hatano, Eiji Takimoto, A Combinatorial Metrical Task System Problem under the Uniform Metric, Proceedings of 27th International Conference on Algorithmic Learning Theory(ALT 2016), 10.1007/978-3-319-46379-7_19, LNCS 9926, 276-287, 2016.10. |

26. | Nir Ailon, Kohei Hatano, Eiji Takimoto, Bandit online optimization over the permutahedron, THEORETICAL COMPUTER SCIENCE, 10.1016/j.tcs.2016.07.033, 650, 92-108, 2016.10, The permutahedron is the convex polytope with vertex set consisting of the vectors (pi (1),..., pi (n)) for all permutations (bijections) pi over {1,...,n} We study a bandit game in which, at each step t, an adversary chooses a hidden weight vector st, a player chooses a vertex pi(t) of the permutahedron and suffers an observed instantaneous loss of Sigma(n)(i=1) pi(t)(i)s(t)(i).We study the problem in two different approaches. In the two approaches, we assume that st is a point in the polytope dual to the permutahedron. Algorithm CombBand of Cesa-Bianchi et al. (2012) guarantees a regret of 0 (n root T log n) after T steps. Unfortunately, CombBand requires at each step an n-by-n matrix permanent computation, a #P-hard problem. Approximating the permanent is possible in the impractical running time of 0 (n(10)), with an additional heavy inverse-polynomial dependence on the sought accuracy. In the first approach, we provide an algorithm of slightly worse regret 0 (n(3/2)root T) but with more realistic time complexity 0 (n(3)) per step. The technical contribution is a bound on the variance of the Plackett-Luce noisy sorting process's 'pseudo loss', obtained by establishing positive semi-definiteness of a family of 3-by-3 matrices of rational functions in exponents of 3 parameters. In the second approach, we present and analyze an algorithm based on Bubeck et al.'s (2012) OSMD approach with a novel projection and decomposition technique for the permutahedron. The second algorithm's running time and regret guarantees are similar to our first algorithm, modulo a numerical line search procedure the running time of which we have not been able to analyze. It is interesting that the two approaches are totally different. The main open problem from this work is whether there exists a bandit algorithm for this problem with both optimal regret of 0 (n root T) and running time of 0 (n(3)) for either regime, or there is an inherent tradeoff between the two performance measures. (C) 2016 Elsevier B.V. All rights reserved.. |

27. | Atsushi Shibagaki, Masayuki Karasuyama, Kohei Hatano, Ichiro Takeuchi, Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling, Proceedings of the 33rd International Conference on Machine Learning (ICML 2016), JMLR W&CP 48, 1577-1586, 2016.07. |

28. | Yao Ma, Tingting Zhao, Kohei Hatano, Masashi Sugiyama, An Online Policy Gradient Algorithm for Markov Decision Processes with Continuous States and Actions, NEURAL COMPUTATION, 10.1162/NECO_a_00808, 28, 3, 563-593, 2016.03, We consider the learning problem under an online Markov decision process (MDP) aimed at learning the time-dependent decision-making policy of an agent that minimizes the regret-the difference from the best fixed policy. The difficulty of online MDP learning is that the reward function changes over time. In this letter, we show that a simple online policy gradient algorithm achieves regret O(root T) for T steps under a certain concavity assumption and O(log T) under a strong concavity assumption. To the best of our knowledge, this is the first work to present an online MDP algorithm that can handle continuous state, action, and parameter spaces with guarantee. We also illustrate the behavior of the proposed online policy gradient method through experiments.. |

29. | Yao Ma, Tingting Zhao, Kohei Hatano, Masashi Sugiyama, An Online Policy Gradient Algorithm for Continuous State and Action Markov Decision Processes, Neural Computation, 10.1162/NECO_a_00808, 28, 3, 563-593, 2016.02. |

30. | Takahiro Fujita, Kohei Hatano, Shuji Kijima, Eiji Takimoto, Online Linear Optimization for Job Scheduling under Precedence Concstraints, Proceedings of 26th International Conference on Algorithmic Learning Theory(ALT 2015), 10.1007/978-3-319-24486-0_22, LNCS 6331, 345-359, 2015.10. |

31. | Issei Matsumoto, Kohei Hatano, Eiji Takimoto, Online Density Estimation of Bradley-Terry Models, Proceedings of the 28th Conference on Learning Theory (COLT 2015), JMLR W&CP 40, 1343-1359, 2015.06. |

32. | Ken-ichiro Moridomi, Kohei Hatano, Eiji Takimoto, Koji Tsuda, Online matrix prediction for sparse loss matrices , Proceedings of the 6th Asian Conference on Machine Learning(ACML 2014)
, JMLR W&CP 39, 250–265, 2015.02. |

33. | Yao Ma, Tingting Zhao, Kohei Hatano, Masashi Sugiyama, An Online Policy Gradient Algorithm for Continuous State and Action Markov Decision Processes, Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2014), 10.1007/978-3-662-44851-9_23, LNCS 8725, 354–369, 2014.10. |

34. | Nir Ailon, Kohei Hatano, Eiji Takimoto, Bandit Online Optimization Over the Permutahedron, Proceedings of the 25th International Conference on Algorithmic Learning Theory (ALT 2014),, LNCS 8776, 215–229, 2014.10. |

35. | Kazuki Teraoka, Kohei Hatano, Eiji Takimoto, Efficient Sampling Method for Monte Carlo Tree Search, IEICE TRANSACTIONS on Information and System, E97-D, 3, 392-298, 2014.03. |

36. | Kazuki Teraoka, Kohei Hatano, Eiji Takimoto, Efficient Sampling Method for Monte Carlo Tree Search Problem, IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 10.1587/transinf.E97.D.392, E97D, 3, 392-398, 2014.03, We consider Monte Carlo tree search problem, a variant of Min-Max tree search problem where the score of each leaf is the expectation of some Bernoulli variables and not explicitly given but can be estimated through (random) playouts. The goal of this problem is, given a game tree and an oracle that returns an outcome of a playout, to find a child node of the root which attains an approximate min-max score. This problem arises in two player games such as computer Go. We propose a simple and efficient algorithm for Monte Carlo tree search problem.. |

37. | Takahiro Fujita, Kohei Hatano, Eiji Takimoto, Combinatorial Online Prediction via Metarounding, Proceedings of the 24th International Conference on Algorithmic Learning Theory (ALT 2013), 68-82, 2013.10. |

38. | Shota Yasutake, Kohei Hatano, Eiji Takimoto, Masayuki Takeda, Online Rank Aggregation , Proceedings of the 4th Asian Conference on Machine Learning(ACML 2012)
, 539-553, 2012.11. |

39. | Yoko Anan, Kohei Hatano, Hideo Bannai, Masayuki Takeda, Polyphonic Music Classification on Symbolic Data Using Dissimilarity Functions, Proceedings of the 13th International Society for Music Information Retrieval Conference (ISMIR 2012), 229-234, 2012.10. |

40. | Daiki Suehiro, Kohei Hatano, Shuji Kijima, Eiji Takimoto, Kiyohito Nagano, Online Prediction under Submodular Constraints, Proceedings of the 23rd International Conference on Algorithmic Learning Theory (ALT 2012)
, 260-274, 2012.10. |

41. | Shota Yasutake, Kohei Hatano, Shuji Kijima, Eiji Takimoto, Masayuki Takeda, , Online Linear Optimization over Permutations , Proceedings of the 22nd International Symposium on Algorithms and Computation (ISAAC 2011)
, 534-543, 2011.11. |

42. | Daiki Suehiro, Kohei Hatano, Eiji Takimoto, Approximate Reduction from AUC Maximization to 1-norm Soft Margin Optimization, Proceedings of the 22nd International Conference on Algorithmic Learning Theory (ALT 2011)
, 324-337, 2011.10. |

43. | Yoko Anan, Kohei Hatano, Hideo Bannai, Masayuki Takeda, Music Genre Classification using Similarity Functions, Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR 2011), 693-698, 2011.10. |

44. | Shin-ichi Yoshida, Kohei Hatano, Eiji Takimoto, Masayuki Takeda, Adaptive Online Prediction Using Weighted Windows, IEICE Transactions on Information and Systems , E94-D, 10, 1917-1923, 2011.10. |

45. | Shin-ichi Yoshida, Kohei Hatano, Eiji Takimoto, Masayuki Takeda, Adaptive Online Prediction Using Weighted Windows, IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 10.1587/transinf.E94.D.1917, E94D, 10, 1917-1923, 2011.10, We propose online prediction algorithms for data streams whose characteristics might change over time. Our algorithms are applications of online learning with experts. In particular, our algorithms combine base predictors over sliding windows with different length as experts. As a result, our algorithms are guaranteed to be competitive with the base predictor with the best fixed-length sliding window in hindsight.. |

46. | Michinari Momma, Kohei Hatano, and Hiroki Nakayama, Ellipsoidal Support Vector Machines , Proceedings of the 2nd Asian Conference on Machine Learning (ACML 2010), 31-46, 2010.11. |

47. | Kazuaki Kashihara, Kohei Hatano, Hideo Bannnai, and Masayuki Takeda, Sparse Substring Pattern Set Discovery using Linear Programming Boosting, Proceedings of the 13th International Conference on Discovery Science (DS 2010), 132-143, 2010.10. |

48. | Kohei Hatano and Eiji Takimoto, Linear Programming Boosting by Column and Row Generation, Proceedings of the Twelfth International Conference on Discovery Science (DS'09) , 2009.10. |

49. | Liwei Wang, Masashi Sugiyama, Cheng Yang, Kohei Hatano, and Jufu Feng, Theory and Algorithm for Learning with Dissimilarity Functions, Neural Computation, Vol. 21, No.5, 1459-1484, 2009.05. |

50. | Liwei Wang, Masashi Sugiyama, Cheng Yang, Kohei Hatano, Jufu Feng, Theory and Algorithm for Learning with Dissimilarity Functions, NEURAL COMPUTATION, 10.1162/neco.2008.08-06-805, 21, 5, 1459-1484, 2009.05, We study the problem of classification when only a dissimilarity function between objects is accessible. That is, data samples are represented not by feature vectors but in terms of their pairwise dissimilarities. We establish sufficient conditions for dissimilarity functions to allow building accurate classifiers. The theory immediately suggests a learning paradigm: construct an ensemble of simple classifiers, each depending on a pair of examples; then find a convex combination of them to achieve a large margin. We next develop a practical algorithm referred to as dissimilarity-based boosting (DBoost) for learning with dissimilarity functions under theoretical guidance. Experiments on a variety of databases demonstrate that the DBoost algorithm is promising for several dissimilarity measures widely used in practice.. |

51. | Jun-ichi Moribe, Kohei Hatano, Eiji Takimoto, and Masayuki Takeda, Smooth Boosting for Margin-Based Ranking, Proceedings of 19th International Conference on Algorithmic Learning Theory, 227-239, 2008.10. |

52. | Kazuyuki Narisawa, Hideo Bannnai, Kohei Hatano, Shunsuke Inenaga, and Masayuki Takeda, String Kernels Based on Variable-Length-Don’t-Care Patterns, Proceedings of the 11th International Conference on Discovery Science, 308-318, 2008.10. |

53. | Kosuke Ishibashi, Kohei Hatano, and Masayuki Takeda, Online Learning of Approximate Maximum p-Norm Margin Classifiers with Biases, Proceedings of the 21st Annual Conference on Learning Theory, 69—80, 2008.07. |

54. | Hayato Kobayashi, Kohei Hatano, Akira Ishino, and Ayumi Shinohara, Autonomous Leaning of Ball Passing by Four-Legged Robots and Trial Reduction by Thinning-Out and Surrogate Functions, Intellegent Autonomous Systems 10, 145—154, 2008.07. |

55. | Kosuke Ishibashi, Kohei Hatano, and Masayuki Takeda, Online Learning of Approximate Maximum Margin Classifiers with Biases, Proceedings of the 2nd International Workshop on Data Mining and Statistical Science, 2007.10. |

56. | Hayato Kobayashi, Kohei Hatano, Akira Ishino, and Ayumi Shinohara, Reducing Trials by Thinning-our in Skill Discovery, Proceedings of the 10th Inthernational Conference on Discovery Science, 2007.10. |

57. | Kazuyuki Narisawa, Hideo Bannnai, Kohei Hatano, and Masayuki Takeda, Unsupervised Spam Detection based on String Alienness Measures, Proceedings of the 10th Inthernational Conference on Discovery Science, 2007.10. |

58. | Kohei Hatano, Smooth Boosting Using an Information-based Criterion, The 17 th international conference on algorithmic learning theory, 10.1007/11894841_25, 304-318, LNAI 4264., 2006.10. |

59. | Hideo Bannai, Kohei Hatano, Shunsuke Inenaga, and Masayuki Takeda, Practical Algorithms for Pattern Based Linear Regression, the 8th international conference on Discovery Science, 3735, 44-56, 44--56, 2005.01. |

60. | Kohei Hatano and Osamu Watanabe, Learning r-of-k Functions by Boosting, Fifteenth International Conference on Algorithmic Learning Theory(ALT2004), 2004.10. |

61. | K Hatano, A simple boosting algorithm using multi-way branching decision trees, THEORY OF COMPUTING SYSTEMS, 10.1007/s00224-003-1064-z, 37, 4, 503-518, 2004.07, We improve the analysis of the decision tree boosting algorithm proposed by Mansour and McAllester. For binary classification problems, the algorithm of Mansour and McAllester constructs a multi-way branching decision tree using a set of multi-class hypotheses. Mansour and McAllester proved that it works under certain conditions. We give a rigorous analysis of the algorithm and simplify the conditions. From this simplification, we can provide a simpler algorithm, for which no prior knowledge on the quality of weak hypotheses is necessary.. |

62. | Kohei Hatano, A Simple Boosting Algorithm Using Multi-Way Branching Decision Trees, Theory of Computing Systems, 2004.01. |

63. | Kohei Hatano and Manfred K. Warmuth, Boosting versus Covering, Seventeenth Annual Conference on Neural Information Processing Systems(NIPS2003), 2003.12. |

64. | Kohei Hatano, Simpler Analysis of The Multi-Way Branching Decision Tree Boosting Algorithm, welfth International Conference on Algorithmic Learning Theory(ALT2001, 2001.12. |

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