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ORCIDEiji Mizutani
Person information
- unicode name: 水谷英二
- affiliation: National Taiwan University of Science and Technology, Taiwan
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2020 – today
- 2024
[j14]Eiji Mizutani, Naoyuki Kubota, Tam Chi Truong:
On Stage-Wise Backpropagation for Improving Cheng's Method for Fully Connected Cascade Networks. Neural Process. Lett. 56(4): 212 (2024)- 2023
- [j13]Eiji Mizutani
, Stuart Dreyfus:
A tutorial on the art of dynamic programming for some issues concerning Bellman's principle of optimality. ICT Express 9(6): 1144-1161 (2023) - [j12]Eiji Mizutani, Kevin Alexander Sánchez Galeano:
A note on a single-shift days-off scheduling problem with sequence-dependent labor costs. J. Sched. 26(3): 315-329 (2023) - 2021
- [j11]Eiji Mizutani, Stuart Dreyfus:
On using dynamic programming for time warping in pattern recognition. Inf. Sci. 580: 684-704 (2021) - 2020
- [j10]Eiji Mizutani:
A Proof on Equivalence of Stagewise Newton and Dreyfus's Successive Approximation Procedures. IEEE Trans. Autom. Control. 65(6): 2716-2723 (2020)
2010 – 2019
- 2019
- [j9]Eiji Mizutani:
A Note on Liao's Recurrent Neural-Network Learning for Discrete Multi-stage Optimal Control Problems. Neural Process. Lett. 50(3): 3009-3018 (2019) - [c20]Eiji Mizutani, Brigitte Trista:
On Two New Dynamic-Programming Procedures As Efficient As the Wagner-Whitin Regeneration-Point Type in Dynamic Lot Sizing. IEEM 2019: 1290-1294 - 2017
- [j8]Eiji Mizutani, Stuart E. Dreyfus:
Totally model-free actor-critic recurrent neural-network reinforcement learning in non-Markovian domains. Ann. Oper. Res. 258(1): 107-131 (2017) - 2015
- [j7]Eiji Mizutani:
On Pantoja's problem allegedly showing a distinction between differential dynamic programming and stagewise Newton methods. Int. J. Control 88(9): 1702-1711 (2015) - [c19]Eiji Mizutani:
On a staged successive approximation procedure for approximate dynamic programming solutions to board game playing. IEEM 2015: 529-533 - 2012
- [c18]Eiji Mizutani:
A block-approximate local Hessian-matrix analysis for CANFIS neuro-fuzzy modular network learning. FUZZ-IEEE 2012: 1-8 - 2011
- [c17]Eiji Mizutani:
On exploiting sparsity in CANFIS neuro-fuzzy modular network learning by second-order stagewise backpropagation. FSKD 2011: 202-206 - [c16]Eiji Mizutani, Jing-Yun Carey Fan:
Online neuro-fuzzy CANFIS hidden-node teaching. FUZZ-IEEE 2011: 2559-2565 - [c15]Eiji Mizutani, James Demmel:
On improving trust-region variable projection algorithms for separable nonlinear least squares learning. IJCNN 2011: 397-404 - 2010
- [c14]Eiji Mizutani, Stuart Dreyfus:
An analysis on negative curvature induced by singularity in multi-layer neural-network learning. NIPS 2010: 1669-1677
2000 – 2009
- 2008
- [j6]Eiji Mizutani, Stuart E. Dreyfus:
Second-order stagewise backpropagation for Hessian-matrix analyses and investigation of negative curvature. Neural Networks 21(2-3): 193-203 (2008) - [j5]Eiji Mizutani, Stuart E. Dreyfus:
Corrigendum to: "Second-order stagewise backpropagation for Hessian-matrix analyses and investigation of negative curvature" [Neural Netw. 21(2-3) (2008) 193-203]. Neural Networks 21(9): 1418 (2008) - 2007
- [c13]Eiji Mizutani, Stuart Dreyfus:
On practical use of stagewise second-order backpropagation for multi-stage neural-network learning. IJCNN 2007: 1302-1307 - [c12]Eiji Mizutani, Jing-Yun Carey Fan:
On exploiting symmetry for multilayer perceptron learning. IJCNN 2007: 2857-2862 - 2006
- [c11]Eiji Mizutani, Stuart Dreyfus:
On derivation of stagewise second-order backpropagation by invariant imbedding for multi-stage neural-network learning. IJCNN 2006: 4762-4769 - 2005
- [c10]Eiji Mizutani, Stuart E. Dreyfus:
Stagewise Newton, differential dynamic programming, and neighboring optimum control for neural-network learning. ACC 2005: 1331-1336 - 2003
- [j4]Eiji Mizutani, James Demmel:
On structure-exploiting trust-region regularized nonlinear least squares algorithms for neural-network learning. Neural Networks 16(5-6): 745-753 (2003) - [c9]Eiji Mizutani, Stuart E. Dreyfus:
On using discretized Cohen-Grossberg node dynamics for model-free actor-critic neural learning in non-Markovian domains. CIRA 2003: 1-6 - [c8]Eiji Mizutani, James Demmel:
Iterative Scaled Trust-Region Learning in Krylov Subspaces via Pearlmutter's Implicit Sparse Hessian-Vector Multiply. NIPS 2003: 209-216 - 2002
- [j3]Eiji Mizutani, Kenichi Nishio:
Multi-illuminant color reproduction for electronic cameras via CANFIS neuro-fuzzy modular network device characterization. IEEE Trans. Neural Networks 13(4): 1009-1022 (2002) - [c7]Eiji Mizutani, Kenichi Nishio:
Fuzzy mixtures of complementary local experts: towards neuro-fuzzy modular networks. FUZZ-IEEE 2002: 1192-1197 - 2000
- [j2]Eiji Mizutani, Hideyuki Takagi, David M. Auslander, Jyh-Shing Roger Jang:
Evolving color recipes. IEEE Trans. Syst. Man Cybern. Part C 30(4): 537-550 (2000) - [c6]Eiji Mizutani, Stuart E. Dreyfus, Kenichi Nishio:
On Derivation of MLP Backpropagation from the Kelley-Bryson Optimal-Control Gradient Formula and Its Application. IJCNN (2) 2000: 167-172 - [c5]Eiji Mizutani, James Demmel:
On Iterative Krylov-Dogleg Trust-Region Steps for Solving Neural Networks Nonlinear Least Squares Problems. NIPS 2000: 605-611
1990 – 1999
- 1999
- [c4]Eiji Mizutani:
Powell's dogleg trust-region steps with the quasi-Newton augmented Hessian for neural nonlinear least-squares learning. IJCNN 1999: 1239-1244 - [c3]Eiji Mizutani:
Sample path-based policy-only learning by actor neural networks. IJCNN 1999: 1245-1250 - 1997
- [j1]Jyh-Shing Roger Jang, Chuen-Tsai Sun, Eiji Mizutani:
Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review]. IEEE Trans. Autom. Control. 42(10): 1482-1484 (1997) - 1995
- [c2]Eiji Mizutani, Jyh-Shing Roger Jang:
Coactive neural fuzzy modeling. ICNN 1995: 760-765 - [c1]Eiji Mizutani, Jyh-Shing Roger Jang, Kenichi Nishio, Hideyuki Takagi, David M. Auslander:
Coactive neuro-fuzzy modelling for colour recipe prediction. ICNN 1995: 2252-2257
Coauthor Index
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