Eiji Takimoto

Shota Yasutake, Kohei Hatano, Eiji Takimoto, Masayuki Takeda: Online Rank Aggregation. Journal of Machine Learning Research - Proceedings Track 25: 539-553 (2012)

Daiki Suehiro, Kohei Hatano, Shuji Kijima, Eiji Takimoto, Kiyohito Nagano: Online Prediction under Submodular Constraints. ALT 2012: 260-274

Shin-ichi Yoshida, Kohei Hatano, Eiji Takimoto, Masayuki Takeda: Adaptive Online Prediction Using Weighted Windows. IEICE Transactions 94-D(10): 1917-1923 (2011)

Kei Uchizawa, Eiji Takimoto, Takao Nishizeki: Size-energy tradeoffs for unate circuits computing symmetric Boolean functions. Theor. Comput. Sci. 412(8-10): 773-782 (2011)

Daiki Suehiro, Kohei Hatano, Eiji Takimoto: Approximate Reduction from AUC Maximization to 1-Norm Soft Margin Optimization. ALT 2011: 324-337

Shota Yasutake, Kohei Hatano, Shuji Kijima, Eiji Takimoto, Masayuki Takeda: Online Linear Optimization over Permutations. ISAAC 2011: 534-543

Kei Uchizawa, Eiji Takimoto: Lower Bounds for Linear Decision Trees via an Energy Complexity Argument. MFCS 2011: 568-579

Hideaki Fukuhara, Eiji Takimoto: Lower Bounds on Quantum Query Complexity for Read-Once Formulas with XOR and MUX Operators. IEICE Transactions 93-D(2): 280-289 (2010)

Hideaki Fukuhara, Eiji Takimoto, Kazuyuki Amano: NPN-Representatives of a Set of Optimal Boolean Formulas. IEICE Transactions 93-A(6): 1008-1015 (2010)

Kei Uchizawa, Takao Nishizeki, Eiji Takimoto: Energy and depth of threshold circuits. Theor. Comput. Sci. 411(44-46): 3938-3946 (2010)

Hideaki Fukuhara, Eiji Takimoto: Lower Bounds on Quantum Query Complexity for Read-Once Decision Trees with Parity Nodes. CATS 2009: 89-98

Kohei Hatano, Eiji Takimoto: Linear Programming Boosting by Column and Row Generation. Discovery Science 2009: 401-408

Kei Uchizawa, Takao Nishizeki, Eiji Takimoto: Energy Complexity and Depth of Threshold Circuits. FCT 2009: 335-345

Kei Uchizawa, Takao Nishizeki, Eiji Takimoto: Size and Energy of Threshold Circuits Computing Mod Functions. MFCS 2009: 724-735

Kei Uchizawa, Eiji Takimoto: Exponential lower bounds on the size of constant-depth threshold circuits with small energy complexity. Theor. Comput. Sci. 407(1-3): 474-487 (2008)

Eiji Takimoto, Yoshihisa Kondo, Satoko Itaya, Ryutaro Suzuki, Sadao Obana: Evaluation of Multi-Channel Flooding for Inter-Vehicle Communication. AINA Workshops 2008: 960-965

Jun-ichi Moribe, Kohei Hatano, Eiji Takimoto, Masayuki Takeda: Smooth Boosting for Margin-Based Ranking. ALT 2008: 227-239

Takayuki Sato, Kazuyuki Amano, Eiji Takimoto, Akira Maruoka: Monotone DNF Formula That Has a Minimal or Maximal Number of Satisfying Assignments. COCOON 2008: 191-203

Marcus Hutter, Rocco A. Servedio, Eiji Takimoto: Editors' Introduction. ALT 2007: 1-8

Kei Uchizawa, Eiji Takimoto: An Exponential Lower Bound on the Size of Constant-Depth Threshold Circuits with Small Energy Complexity. IEEE Conference on Computational Complexity 2007: 169-178

Marcus Hutter, Rocco A. Servedio, Eiji Takimoto (Eds.): Algorithmic Learning Theory, 18th International Conference, ALT 2007, Sendai, Japan, October 1-4, 2007, Proceedings. Lecture Notes in Computer Science 4754, Springer 2007, isbn 978-3-540-75224-0

Shigeaki Harada, Eiji Takimoto, Akira Maruoka: Online Allocation with Risk Information. IEICE Transactions 89-D(8): 2340-2347 (2006)

Ricard Gavaldà, Eiji Takimoto: Foreword. Theor. Comput. Sci. 364(1): 1-2 (2006)

Shigeaki Harada, Eiji Takimoto, Akira Maruoka: Aggregating Strategy for Online Auctions. COCOON 2006: 33-41

Shigeaki Harada, Eiji Takimoto, Akira Maruoka: Online Allocation with Risk Information. ALT 2005: 343-355

Kenshi Matsuo, Tetsuya Koyama, Eiji Takimoto, Akira Maruoka: Relationships between Horn Formulas and XOR-MDNF Formulas. IEICE Transactions 87-D(2): 343-351 (2004)

Eiji Takimoto, Syuhei Koya, Akira Maruoka: Boosting Based on Divide and Merge. ALT 2004: 127-141

Eiji Takimoto, Manfred K. Warmuth: Path Kernels and Multiplicative Updates. Journal of Machine Learning Research 4: 773-818 (2003)

Eiji Takimoto, Akira Maruoka: Top-down decision tree learning as information based boosting. Theor. Comput. Sci. 292(2): 447-464 (2003)

Ricard Gavaldà, Klaus P. Jantke, Eiji Takimoto (Eds.): Algorithmic Learning Theory, 14th International Conference, ALT 2003, Sapporo, Japan, October 17-19, 2003, Proceedings. Lecture Notes in Computer Science 2842, Springer 2003, isbn 3-540-20291-9

Eiji Takimoto, Manfred K. Warmuth: Predicting nearly as well as the best pruning of a planar decision graph. Theor. Comput. Sci. 288(2): 217-235 (2002)

Eiji Takimoto, Manfred K. Warmuth: Path Kernels and Multiplicative Updates. COLT 2002: 74-89

Akira Maruoka, Eiji Takimoto: On-Line Algorithm to Predict Nearly as Well as the Best Pruning of a Decision Tree. Progress in Discovery Science 2002: 296-306

Eiji Takimoto, Akira Maruoka: Top-Down Decision Tree Boosting and Its Applications. Progress in Discovery Science 2002: 327-337

Eiji Takimoto, Akira Maruoka, Volodya Vovk: Predicting nearly as well as the best pruning of a decision tree through dynamic programming scheme. Theor. Comput. Sci. 261(1): 179-209 (2001)

Eiji Takimoto, Yoshifumi Sakai, Akira Maruoka: The learnability of exclusive-or expansions based on monotone DNF formulas. Theor. Comput. Sci. 241(1-2): 37-50 (2000)

Eiji Takimoto, Manfred K. Warmuth: The Last-Step Minimax Algorithm. ALT 2000: 279-290

Eiji Takimoto, Manfred K. Warmuth: The Minimax Strategy for Gaussian Density Estimation. pp. COLT 2000: 100-106

Jun Mizuno, Tasuya Watanabe, Kazuya Ueki, Kazuyuki Amano, Eiji Takimoto, Akira Maruoka: On-Line Estimation of Hidden Markov Model Parameters. Discovery Science 2000: 155-169

Yoshifumi Sakai, Eiji Takimoto, Akira Maruoka: Proper Learning Algorithm for Functions of k Terms under Smooth Distributions. Inf. Comput. 152(2): 188-204 (1999)

Eiji Takimoto, Manfred K. Warmuth: Predicting Nearly as well as the best Pruning of a Planar Decision Graph. ALT 1999: 335-346

Akira Maruoka, Eiji Takimoto: Structured Weight-Based Prediction Algorithms. ALT 1998: 127-142

Eiji Takimoto, Akira Maruoka: On the Boosting Algorithm for Multiclass Functions Based on Information-Theoretic Criterion for Approxiamtion. Discovery Science 1998: 256-267

Eiji Takimoto, Akira Miyashiro, Akira Maruoka, Yoshifumi Sakai: Learning Orthogonal F-Horn Formulas. Theor. Comput. Sci. 185(1): 177-190 (1997)

Eiji Takimoto, Ken'ichi Hirai, Akira Maruoka: A Simple Algorithm for Predicting Nearly as Well as the Best Pruning Labeled with the Best Prediction Values of a Decision Tree. ALT 1997: 385-400

Eiji Takimoto, Yoshifumi Sakai, Akira Maruoka: Learnability of Exclusive-Or Expansion Based on Monotone DNF Formulas. ALT 1996: 12-25

Eiji Takimoto, Akira Maruoka: On the Sample Complexity of Consistent Learning with One-Sided Error. IEICE Transactions 78-D(5): 518-525 (1995)

Akira Miyashiro, Eiji Takimoto, Yoshifumi Sakai, Akira Maruoka: Learning Orthogonal F-Horn Formulas. ALT 1995: 110-122

Yoshifumi Sakai, Eiji Takimoto, Akira Maruoka: Proper Learning Algorithm for Functions of k Terms Under Smooth Distributions. COLT 1995: 206-213

Eiji Takimoto, Ichiro Tajika, Akira Maruoka: Mutual Information Gaining Algorithm and Its Relation to PAC-Learning Algorithm. AII/ALT 1994: 547-559

Eiji Takimoto, Akira Maruoka: Relationships between learning and information compression based on PAC learning model. Systems and Computers in Japan 24(8): 47-58 (1993)

Eiji Takimoto, Akira Maruoka: On the Sample Complexity of Consistent Learning with One-Sided Error. ALT 1993: 265-278

Eiji Takimoto, Akira Maruoka: Conservativeness and Monotonicity for Learning Algorithms. COLT 1993: 377-383