Hiroshi Tsukimoto

18 papers B 3C 1Journal 7Unranked 7
YearRankTypeTitle / Venue / Authors
2016 J jnl
An algorithm for 3-SAT problems.
CoRR
Hiroshi Tsukimoto
2004 J jnl
Nonparametric regression analysis of functional brain images.
Syst. Comput. Jpn.
Hiroshi Tsukimoto, Mitsuru Kakimoto, Chie Morita, Yoshiaki Kikuchi
2003 J jnl
The functional localization of neural networks using genetic algorithms.
Neural Networks
Hiroshi Tsukimoto, Hisaaki Hatano
2002 — conf
Rule Discovery from fMRI Brain Images by Logical Regression Analysis.
Progress in Discovery Science
Hiroshi Tsukimoto, Mitsuru Kakimoto, Chie Morita, Yoshiaki Kikuchi
2001 J jnl
Pattern reasoning: Logical reasoning of neural networks.
Syst. Comput. Jpn.
Hiroshi Tsukimoto
2000 — conf
Data Mining from Functional Brain Images.
MDM/KDD
Mitsuru Kakimoto, Chie Morita, Yoshiaki Kikuchi, Hiroshi Tsukimoto
2000 J jnl
Extracting rules from trained neural networks.
IEEE Trans. Neural Networks Learn. Syst.
Hiroshi Tsukimoto
2000 B conf
Knowledge Discovery from fMRI Brain Images by Logical Regression Analysis.
Discovery Science
Hiroshi Tsukimoto, Mitsuru Kakimoto, Chie Morita, Yoshiaki Kikuchi, Eiko Hatakeyama, Yoshifumi Miyazaki
1999 — conf
Pattern Reasoning: A New Solution for Knowledge Acquisition Problem.
RSFDGrC
Hiroshi Tsukimoto
1999 B conf
Rule Extraction from Prediction Models.
PAKDD
Hiroshi Tsukimoto
1998 J jnl
Knowledge discovery from numerical data.
Knowl. Based Syst.
Chie Morita, Hiroshi Tsukimoto
1998 B conf
The Discovery of Rules from Brain Images.
Discovery Science
Hiroshi Tsukimoto, Chie Morita
1997 J jnl
An inductive learning algorithm based on regression analysis.
Syst. Comput. Jpn.
Hiroshi Tsukimoto, Chie Morita, Nobuhiro Shimogori
1997 — conf
Extracting Propositions from Trained Neural Networks.
IJCAI
Hiroshi Tsukimoto
1996 C conf
Knowledge Discovery from Numerical Data.
IEA/AIE
Chie Morita, Hiroshi Tsukimoto
1994 — conf
The Discovery of Logical Propositions in Numerical Data.
KDD Workshop
Hiroshi Tsukimoto
1993 — conf
Efficient algorithms for inductive learning - an application of multi-linear functions to inductive learning.
Machine Intelligence 14
Hiroshi Tsukimoto, Chie Morita
1992 — conf
The Discovery of Propositions in Noisy Data.
Machine Intelligence 13
Hiroshi Tsukimoto, Chie Morita