Takato Tatsumi

18 papers A 1B 5Journal 4Unranked 8

Year	Rank	Type	Title / Venue / Authors
2020	B	`conf`	Local Covering: Adaptive Rule Generation Method Using Existing Rules for XCS. CEC Masakazu Tadokoro, Satoshi Hasegawa, Takato Tatsumi, Hiroyuki Sato, Keiki Takadama
2019	B	`conf`	Comparison of Statistical Table- and Non-Statistical Table-based XCS in Noisy Environments. CEC Takato Tatsumi, Keiki Takadama
2019	B	`conf`	Knowledge Extraction from XCSR Based on Dimensionality Reduction and Deep Generative Models. CEC Masakazu Tadokoro, Satoshi Hasegawa, Takato Tatsumi, Hiroyuki Sato, Keiki Takadama
2019	—	`conf`	XCS-CR for handling input, output, and reward noise. GECCO (Companion) Takato Tatsumi, Keiki Takadama
2018	—	`conf`	Classifier generalization for comprehensive classifiers subsumption in XCS. GECCO (Companion) Caili Zhang, Takato Tatsumi, Hiyoyuki Sato, Tim Kovacs, Keiki Takadama
2018	—	`conf`	Study of Analytical Methods on the Relationship between Sleep Quality and Stress with a focus on Human Circadian Rhythm. AAAI Spring Symposia Ryo Takano, Satoshi Hasegawa, Yuta Umenai, Takato Tatsumi, Keiki Takadama, Toru Shimuta, Toru Yabe, Hideo Matsumoto
2018	—	`conf`	XCS for Missing Attributes in Data. SCIS&ISIS Takato Tatsumi, Keiki Takadama
2018	—	`conf`	XCS-CR: determining accuracy of classifier by its collective reward in action set toward environment with action noise. GECCO (Companion) Takato Tatsumi, Tim Kovacs, Keiki Takadama
2018	—	`conf`	XCSR based on compressed input by deep neural network for high dimensional data. GECCO (Companion) Kazuma Matsumoto, Ryo Takano, Takato Tatsumi, Hiroyuki Sato, Tim Kovacs, Keiki Takadama
2017	B	`conf`	Applying variance-based Learning Classifier System without Convergence of Reward Estimation into various Reward distribution. CEC Takato Tatsumi, Hiroyuki Sato, Tim Kovacs, Keiki Takadama
2017	J	`jnl`	Approach to Clustering with Variance-Based XCS. J. Adv. Comput. Intell. Intell. Informatics Caili Zhang, Takato Tatsumi, Masaya Nakata, Keiki Takadama
2017	A	`conf`	Automatic adjustment of selection pressure based on range of reward in learning classifier system. GECCO Takato Tatsumi, Hiroyuki Sato, Keiki Takadama
2017	J	`jnl`	Learning Classifier System Based on Mean of Reward. J. Adv. Comput. Intell. Intell. Informatics Takato Tatsumi, Hiroyuki Sato, Keiki Takadama
2017	J	`jnl`	Supporting the Exploration of the Learning Goals for a Continuous Learner Toward Creative Learning. J. Adv. Comput. Intell. Intell. Informatics Takato Okudo, Tomohiro Yamaguchi, Akinori Murata, Takato Tatsumi, Fumito Uwano, Keiki Takadama
2017	J	`jnl`	XCSR Learning from Compressed Data Acquired by Deep Neural Network. J. Adv. Comput. Intell. Intell. Informatics Kazuma Matsumoto, Takato Tatsumi, Hiroyuki Sato, Tim Kovacs, Keiki Takadama
2016	—	`conf`	Extracting Different Abstracted Level Rule with Variance-Based LCS. SCIS&ISIS Caili Zhang, Takato Tatsumi, Masaya Nakata, Keiki Takadama, Hiroyuki Sato, Tim Kovacs
2016	—	`conf`	Variance-based Learning Classifier System without Convergence of Reward Estimation. GECCO (Companion) Takato Tatsumi, Takahiro Komine, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, Keiki Takadama
2015	B	`conf`	Handling different level of unstable reward environment through an estimation of reward distribution in XCS. CEC Takato Tatsumi, Takahiro Komine, Hiroyuki Sato, Keiki Takadama