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ORCIDKeiji Tatsumi
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2020 – today
- 2024
[j14]Keiji Tatsumi
, Ryota Masui, Haruhisa Miyahara, Yeboon Yun:
Multiclass piecewise-linear SVMs with the restricted feasible region for the reinforcement degrees in constructing large-scale structures. Knowl. Based Syst. 300: 112197 (2024)- [c25]Keiji Tatsumi, Satoshi Maruyama, Yeboon Yun:
Multiclass piecewise-linear SVMs based on division of data space for determining the reinforcement degrees of large-scale structures. IJCNN 2024: 1-6 - 2022
- [j13]Keiji Tatsumi, Shunsuke Tsujioka, Ryota Masui, Yoshifumi Kusunoki, Yeboon Yun:
Determination of reinforcement degrees in constructing large-scale structures by using multiclass support vector machines. Knowl. Based Syst. 249: 108807 (2022) - [c24]Yoshifumi Kusunoki, Katsuhiko Kojima, Keiji Tatsumi:
Transductive Learning Based on Low-Rank Representation with Convex Constraints. IUKM 2022: 291-301 - [c23]Keiji Tatsumi, Nao Kinoshita:
Shift-invariant grey wolf optimizer exploiting reference points and random selection of step-sizes. SICE 2022: 1201-1206 - 2021
- [c22]Keiji Tatsumi, Ryota Masui, Shunsuke Tsujioka, Yoshifumi Kusunoki, Yeboon Yun:
Multiclass Support Vector Machines for Determination of Reinforcement Degree in Constructing Large-scale Structures Based on Data Characteristics. SMC 2021: 1513-1518 - 2020
- [c21]Nao Kinoshita, Keiji Tatsumi:
Chaotic particle swarm optimization using a rotation transformation based on two best solutions. SMC 2020: 1135-1140 - [c20]Katsuhiko Kojima, Yoshifumi Kusunoki, Keiji Tatsumi:
Manifold Learning with Intrinsic Distance Estimation Using Kernelized Linear Model for Metric Tensors. SMC 2020: 3558-3563
2010 – 2019
- 2019
- [j12]Yusaku Hirai, Toshimasa Matsuoka, Sadahiro Tani, Shodai Isami, Keiji Tatsumi, Masayuki Ueda, Takatsugu Kamata:
A Biomedical Sensor System With Stochastic A/D Conversion and Error Correction by Machine Learning. IEEE Access 7: 21990-22001 (2019) - [j11]Keiji Tatsumi, Toshimasa Matsuoka:
A Software Level Calibration Based on Bayesian Regression for a Successive Stochastic Approximation Analog-to-Digital Converter System. IEEE Trans. Cybern. 49(4): 1200-1211 (2019) - [c19]Yoshifumi Kusunoki, Keiji Tatsumi:
Scalarization for Approximate Multiobjective Multiclass Support Vector Machine Using the Large-k Norm. EUSFLAT Conf. 2019 - [c18]Keiji Tatsumi, Takahumi Sugimoto, Yoshifumi Kusunoki:
Approximate Multiobjective Multiclass Support Vector Machine Restricting Classifier Candidates Based on k-Means Clustering. IUKM 2019: 271-283 - 2018
- [j10]Yoshifumi Kusunoki, Chiharu Wakou, Keiji Tatsumi:
Maximum-Margin Model for Nearest Prototype Classifiers. J. Adv. Comput. Intell. Intell. Informatics 22(4): 565-577 (2018) - [c17]Yoshifumi Kusunoki, Keiji Tatsumi:
A Multi-class Support Vector Machine Based on Geometric Margin Maximization. IUKM 2018: 101-113 - [c16]Keiji Tatsumi:
Modified Perturbation-Based Chaotic System Using the Quasi-Newton Method with the Symmetric Rank-One Formula for Global Optimization. SMC 2018: 3335-3340 - [c15]Yasuhiro Matsugi, Takafumi Sugimoto, Yuxiao Qi, Yoshifumi Kusunoki, Keiji Tatsumi:
Approximate Multiobjective Multiclass SVM by Using the Reference Point Method. SMC 2018: 3535-3540 - 2017
- [j9]Sadahiro Tani, Toshimasa Matsuoka, Yusaku Hirai, Toshifumi Kurata, Keiji Tatsumi, Tomohiro Asano, Masayuki Ueda, Takatsugu Kamata:
Behavior-Level Analysis of a Successive Stochastic Approximation Analog-to-Digital Conversion System for Multi-Channel Biomedical Data Acquisition. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 100-A(10): 2073-2085 (2017) - [j8]Keiji Tatsumi, Tetsuzo Tanino:
A Perturbation Based Chaotic System Exploiting the Quasi-Newton Method for Global Optimization. Int. J. Bifurc. Chaos 27(4): 1750047:1-1750047:23 (2017) - [j7]Keiji Tatsumi:
A Weaker Sufficient Condition for the Chaoticity of Extended Perturbation-Based Updating System for Global Optimization. Int. J. Bifurc. Chaos 27(6): 1750085:1-1750085:24 (2017) - [c14]Keiji Tatsumi, Yuki Kawashita, Takahumi Sugimoto:
A Multiobjective Multiclass Support Vector Machine Restricting Classifier Candidates Based on k-Means Clustering. ICONIP (1) 2017: 297-304 - [c13]Keiji Tatsumi, Toshimasa Matsuoka, Sadahiro Tani:
A calibration with an adaptive data selection based on Bayes estimation for a successive stochastic approximation ADC system. IFSA-SCIS 2017: 1-6 - [c12]Chiharu Wakou, Yoshifumi Kusunoki, Keiji Tatsumi:
Learning prototype-based classifiers by margin maximization. IFSA-SCIS 2017: 1-6 - [c11]Takatsugu Kamata, Masayuki Ueda, Yusaku Hirai, Sadahiro Tani, Tomohiro Asano, Shodai Isami, Toshifumi Kurata, Keiji Tatsumi, Toshimasa Matsuoka:
An analog front-end employing 87 dB SNDR stochastic SAR-ADC for a biomedical sensor. NEWCAS 2017: 301-304 - 2015
- [j6]Keiji Tatsumi, Takeru Ibuki, Tetsuzo Tanino:
Particle swarm optimization with stochastic selection of perturbation-based chaotic updating system. Appl. Math. Comput. 269: 904-929 (2015) - [c10]Keiji Tatsumi, Takeru Ibuki, Tetsuzo Tanino:
Chaotic Multi-swarm Particle Swarm Optimization Using Combined Quartic Functions. SMC 2015: 2096-2101 - 2014
- [c9]Yoichi Izunaga, Keisuke Sato, Keiji Tatsumi, Yoshitsugu Yamamoto:
Row and Column Generation Algorithm for Maximization of Minimum Margin for Ranking Problems. OR 2014: 249-255 - 2013
- [j5]Keiji Tatsumi, Takeru Ibuki, Tetsuzo Tanino:
A chaotic particle swarm optimization exploiting a virtual quartic objective function based on the personal and global best solutions. Appl. Math. Comput. 219(17): 8991-9011 (2013) - [j4]Keiji Tatsumi, Tetsuzo Tanino:
A sufficient condition for Chaos in the Gradient Model with perturbation Method for Global Optimization. Int. J. Bifurc. Chaos 23(6) (2013) - [c8]Keiji Tatsumi, Takeru Ibuki, Satoshi Nakashima, Tetsuzo Tanino:
Improved Chaotic Particle Swarm Optimization with a Perturbation-Based Chaotic System for a Virtual Quartic Function. SMC 2013: 208-213 - 2012
- [c7]Keiji Tatsumi, Takeru Ibuki, Tetsuzo Tanino:
Chaotic particle swarm optimization with an intensive search around the personal and global bests. SMC 2012: 1333-1338 - 2011
- [c6]Keiji Tatsumi, Masato Tai, Tetsuzo Tanino:
Nonlinear extension of multiobjective multiclass support vector machine based on the one-against-all method. IJCNN 2011: 1570-1576 - 2010
- [c5]Keiji Tatsumi, Masato Tai, Tetsuzo Tanino:
Multiobjective multiclass support vector machine based on the one-against-all method. IJCNN 2010: 1-7 - [c4]Keiji Tatsumi, Ryo Kawachi, Tetsuzo Tanino:
Nonlinear extension of multiobjective multiclass Support Vector Machine. SMC 2010: 1338-1343
2000 – 2009
- 2009
- [c3]Keiji Tatsumi, Ryo Kawachi, Kenji Hayashida, Tetsuzo Tanino:
Multiobjective Multiclass Soft-Margin Support Vector Machine and Its Solving Technique Based on Benson's Method. MDAI 2009: 360-371 - [c2]Keiji Tatsumi, Takashi Yukami, Tetsuzo Tanino:
Restarting Multi-type Particle Swarm Optimization Using an Adaptive Selection of Particle Type. SMC 2009: 923-928 - 2008
- [j3]Keiji Tatsumi, Tetsuzo Tanino:
Improved Projection Hopfield Network for the Quadratic Assignment Problem. Int. J. Inf. Technol. Decis. Mak. 7(1): 53-70 (2008) - [c1]Keiji Tatsumi, Ryo Kawachi, Kenji Hayashida, Tetsuzo Tanino:
Multiobjective Multiclass Soft-Margin Support Vector Machine Maximizing Pair-Wise Interclass Margins. ICONIP (1) 2008: 970-977 - 2006
- [j2]Atsushi Moritani, Tetsuzo Tanino, Keiji Tatsumi:
Cooperative fuzzy games extended from ordinary cooperative games with restrictions on coalitions. Kybernetika 42(4): 461-473 (2006)
1990 – 1999
- 1996
- [j1]Keiji Tatsumi, Masao Fukushima:
A successive projection method for binary pattern recognition with multilayer feedforward neural networks. Int. J. Syst. Sci. 27(10): 917-923 (1996)
Coauthor Index
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