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2020 – today
- 2020
[c14]Masakazu Tadokoro, Satoshi Hasegawa, Takato Tatsumi, Hiroyuki Sato, Keiki Takadama:
Local Covering: Adaptive Rule Generation Method Using Existing Rules for XCS. CEC 2020: 1-8
2010 – 2019
- 2019
- [c13]Takato Tatsumi, Keiki Takadama:
Comparison of Statistical Table- and Non-Statistical Table-based XCS in Noisy Environments. CEC 2019: 1875-1882 - [c12]Masakazu Tadokoro, Satoshi Hasegawa, Takato Tatsumi, Hiroyuki Sato, Keiki Takadama:
Knowledge Extraction from XCSR Based on Dimensionality Reduction and Deep Generative Models. CEC 2019: 1883-1890 - [c11]Takato Tatsumi, Keiki Takadama:
XCS-CR for handling input, output, and reward noise. GECCO (Companion) 2019: 1303-1311 - 2018
- [c10]Ryo Takano, Satoshi Hasegawa, Yuta Umenai, Takato Tatsumi, Keiki Takadama, Toru Shimuta, Toru Yabe, Hideo Matsumoto:
Study of Analytical Methods on the Relationship between Sleep Quality and Stress with a focus on Human Circadian Rhythm. AAAI Spring Symposia 2018 - [c9]Kazuma Matsumoto, Ryo Takano, Takato Tatsumi, Hiroyuki Sato, Tim Kovacs, Keiki Takadama:
XCSR based on compressed input by deep neural network for high dimensional data. GECCO (Companion) 2018: 1418-1425 - [c8]Takato Tatsumi, Tim Kovacs, Keiki Takadama:
XCS-CR: determining accuracy of classifier by its collective reward in action set toward environment with action noise. GECCO (Companion) 2018: 1457-1464 - [c7]Caili Zhang, Takato Tatsumi, Hiyoyuki Sato, Tim Kovacs, Keiki Takadama:
Classifier generalization for comprehensive classifiers subsumption in XCS. GECCO (Companion) 2018: 1854-1861 - [c6]Takato Tatsumi, Keiki Takadama:
XCS for Missing Attributes in Data. SCIS&ISIS 2018: 329-334 - 2017
- [j4]Kazuma Matsumoto, Takato Tatsumi, Hiroyuki Sato, Tim Kovacs, Keiki Takadama:
XCSR Learning from Compressed Data Acquired by Deep Neural Network. J. Adv. Comput. Intell. Intell. Informatics 21(5): 856-867 (2017) - [j3]Caili Zhang, Takato Tatsumi, Masaya Nakata, Keiki Takadama:
Approach to Clustering with Variance-Based XCS. J. Adv. Comput. Intell. Intell. Informatics 21(5): 885-894 (2017) - [j2]Takato Tatsumi, Hiroyuki Sato, Keiki Takadama:
Learning Classifier System Based on Mean of Reward. J. Adv. Comput. Intell. Intell. Informatics 21(5): 895-906 (2017) - [j1]Takato Okudo, Tomohiro Yamaguchi, Akinori Murata, Takato Tatsumi, Fumito Uwano, Keiki Takadama:
Supporting the Exploration of the Learning Goals for a Continuous Learner Toward Creative Learning. J. Adv. Comput. Intell. Intell. Informatics 21(5): 907-916 (2017) - [c5]Takato Tatsumi, Hiroyuki Sato, Tim Kovacs, Keiki Takadama:
Applying variance-based Learning Classifier System without Convergence of Reward Estimation into various Reward distribution. CEC 2017: 2630-2637 - [c4]Takato Tatsumi, Hiroyuki Sato, Keiki Takadama:
Automatic adjustment of selection pressure based on range of reward in learning classifier system. GECCO 2017: 505-512 - 2016
- [c3]Takato Tatsumi, Takahiro Komine, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, Keiki Takadama:
Variance-based Learning Classifier System without Convergence of Reward Estimation. GECCO (Companion) 2016: 67-68 - [c2]Caili Zhang, Takato Tatsumi, Masaya Nakata, Keiki Takadama, Hiroyuki Sato, Tim Kovacs:
Extracting Different Abstracted Level Rule with Variance-Based LCS. SCIS&ISIS 2016: 160-165 - 2015
- [c1]Takato Tatsumi, Takahiro Komine, Hiroyuki Sato, Keiki Takadama:
Handling different level of unstable reward environment through an estimation of reward distribution in XCS. CEC 2015: 2973-2980
Coauthor Index
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