Eiji Mizutani - RankMe

37 papers B 12C 1Journal 14Unranked 10

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	On Stage-Wise Backpropagation for Improving Cheng's Method for Fully Connected Cascade Networks. Neural Process. Lett. Eiji Mizutani, Naoyuki Kubota, Tam Chi Truong
2023	J	`jnl`	A note on a single-shift days-off scheduling problem with sequence-dependent labor costs. J. Sched. Eiji Mizutani, Kevin Alexander Sánchez Galeano
2023	J	`jnl`	A tutorial on the art of dynamic programming for some issues concerning Bellman's principle of optimality. ICT Express Eiji Mizutani, Stuart Dreyfus
2021	J	`jnl`	On using dynamic programming for time warping in pattern recognition. Inf. Sci. Eiji Mizutani, Stuart Dreyfus
2020	J	`jnl`	A Proof on Equivalence of Stagewise Newton and Dreyfus's Successive Approximation Procedures. IEEE Trans. Autom. Control. Eiji Mizutani
2019	J	`jnl`	A Note on Liao's Recurrent Neural-Network Learning for Discrete Multi-stage Optimal Control Problems. Neural Process. Lett. Eiji Mizutani
2019	—	`conf`	On Two New Dynamic-Programming Procedures As Efficient As the Wagner-Whitin Regeneration-Point Type in Dynamic Lot Sizing. IEEM Eiji Mizutani, Brigitte Trista
2017	J	`jnl`	Totally model-free actor-critic recurrent neural-network reinforcement learning in non-Markovian domains. Ann. Oper. Res. Eiji Mizutani, Stuart E. Dreyfus
2015	J	`jnl`	On Pantoja's problem allegedly showing a distinction between differential dynamic programming and stagewise Newton methods. Int. J. Control Eiji Mizutani
2015	—	`conf`	On a staged successive approximation procedure for approximate dynamic programming solutions to board game playing. IEEM Eiji Mizutani
2012	B	`conf`	A block-approximate local Hessian-matrix analysis for CANFIS neuro-fuzzy modular network learning. FUZZ-IEEE Eiji Mizutani
2011	—	`conf`	On exploiting sparsity in CANFIS neuro-fuzzy modular network learning by second-order stagewise backpropagation. FSKD Eiji Mizutani
2011	B	`conf`	On improving trust-region variable projection algorithms for separable nonlinear least squares learning. IJCNN Eiji Mizutani, James Demmel
2011	B	`conf`	Online neuro-fuzzy CANFIS hidden-node teaching. FUZZ-IEEE Eiji Mizutani, Jing-Yun Carey Fan
2010	—	`conf`	An analysis on negative curvature induced by singularity in multi-layer neural-network learning. NIPS Eiji Mizutani, Stuart Dreyfus
2008	J	`jnl`	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 Eiji Mizutani, Stuart E. Dreyfus
2008	J	`jnl`	Second-order stagewise backpropagation for Hessian-matrix analyses and investigation of negative curvature. Neural Networks Eiji Mizutani, Stuart E. Dreyfus
2007	B	`conf`	On exploiting symmetry for multilayer perceptron learning. IJCNN Eiji Mizutani, Jing-Yun Carey Fan
2007	B	`conf`	On practical use of stagewise second-order backpropagation for multi-stage neural-network learning. IJCNN Eiji Mizutani, Stuart Dreyfus
2006	B	`conf`	On derivation of stagewise second-order backpropagation by invariant imbedding for multi-stage neural-network learning. IJCNN Eiji Mizutani, Stuart Dreyfus
2005	B	`conf`	Second-order backpropagation algorithms for a stagewise-partitioned separable Hessian matrix. IJCNN Eiji Mizutani, Stuart E. Dreyfus, James Weldon Demmel
2005	C	`conf`	Stagewise Newton, differential dynamic programming, and neighboring optimum control for neural-network learning. ACC Eiji Mizutani, Stuart E. Dreyfus
2004	B	`conf`	Two stochastic dynamic programming problems by model-free actor-critic recurrent-network learning in non-Markovian settings. IJCNN Eiji Mizutani, Stuart E. Dreyfus
2003	—	`conf`	Iterative Scaled Trust-Region Learning in Krylov Subspaces via Pearlmutter's Implicit Sparse Hessian-Vector Multiply. NIPS Eiji Mizutani, James Demmel
2003	B	`conf`	On sparsity-exploiting memory-efficient trust-region regularized nonlinear least squares algorithms for neural-network learning. IJCNN Eiji Mizutani, James Weldon Demmel
2003	J	`jnl`	On structure-exploiting trust-region regularized nonlinear least squares algorithms for neural-network learning. Neural Networks Eiji Mizutani, James Demmel
2003	—	`conf`	On using discretized Cohen-Grossberg node dynamics for model-free actor-critic neural learning in non-Markovian domains. CIRA Eiji Mizutani, Stuart E. Dreyfus
2002	B	`conf`	Fuzzy mixtures of complementary local experts: towards neuro-fuzzy modular networks. FUZZ-IEEE Eiji Mizutani, Kenichi Nishio
2002	J	`jnl`	Multi-illuminant color reproduction for electronic cameras via CANFIS neuro-fuzzy modular network device characterization. IEEE Trans. Neural Networks Eiji Mizutani, Kenichi Nishio
2000	J	`jnl`	Evolving color recipes. IEEE Trans. Syst. Man Cybern. Part C Eiji Mizutani, Hideyuki Takagi, David M. Auslander, Jyh-Shing Roger Jang
2000	—	`conf`	On Derivation of MLP Backpropagation from the Kelley-Bryson Optimal-Control Gradient Formula and Its Application. IJCNN (2) Eiji Mizutani, Stuart E. Dreyfus, Kenichi Nishio
2000	—	`conf`	On Iterative Krylov-Dogleg Trust-Region Steps for Solving Neural Networks Nonlinear Least Squares Problems. NIPS Eiji Mizutani, James Demmel
1999	B	`conf`	Powell's dogleg trust-region steps with the quasi-Newton augmented Hessian for neural nonlinear least-squares learning. IJCNN Eiji Mizutani
1999	B	`conf`	Sample path-based policy-only learning by actor neural networks. IJCNN Eiji Mizutani
1997	J	`jnl`	Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review]. IEEE Trans. Autom. Control. Jyh-Shing Roger Jang, Chuen-Tsai Sun, Eiji Mizutani
1995	—	`conf`	Coactive neural fuzzy modeling. ICNN Eiji Mizutani, Jyh-Shing Roger Jang
1995	—	`conf`	Coactive neuro-fuzzy modelling for colour recipe prediction. ICNN Eiji Mizutani, Jyh-Shing Roger Jang, Kenichi Nishio, Hideyuki Takagi, David M. Auslander