Yoshifusa Ito

发表

Learning of Bayesian Discriminant Functions by a Layered Neural Network

Cidambi Srinivasan, Hiroyuki Izumi, Yoshifusa Ito, 2007, ICONIP.

Discriminant Analysis by a Neural Network with Mahalanobis Distance

Cidambi Srinivasan, Hiroyuki Izumi, Yoshifusa Ito, 2006, ICANN.

A Weak Condition on Linear Independence of Unscaled Shifts of a Function and Finite Mappings by Neural Networks

Yoshifusa Ito, Yoshifusa Ito, 2002, ICANN.

Estimation of Hidden Markov Chains by a Neural Network

Cidambi Srinivasan, Hiroyuki Izumi, Yoshifusa Ito, 2014, ICONIP.

Simultaneous Learning of Several Bayesian and Mahalanobis Discriminant Functions by a Neural Network with Memory Nodes

Cidambi Srinivasan, Hiroyuki Izumi, Yoshifusa Ito, 2012, ICONIP.

Nonlinearity creates linear independence

Yoshifusa Ito, Yoshifusa Ito, 1996, Adv. Comput. Math..

Approximation of continuous functions on Rd by linear combinations of shifted rotations of a sigmoid function with and without scaling

Yoshifusa Ito, Yoshifusa Ito, 1992, Neural Networks.

A Neural Network having Fewer Inner Constants to be Trained and Bayesian Decision

Hiroyuki Izumi, Yoshifusa Ito, Crinivasan Srinivasan, 2007, 2007 International Joint Conference on Neural Networks.

Bayesian Learning of Neural Networks Adapted to Changes of Prior Probabilities

Cidambi Srinivasan, Hiroyuki Izumi, Yoshifusa Ito, 2005, ICANN.

A New Algorithm for Learning Mahalanobis Discriminant Functions by a Neural Network

Cidambi Srinivasan, Hiroyuki Izumi, Yoshifusa Ito, 2011, ICONIP.

Simultaneous Approximations of Polynomials and Derivatives and Their Applications to Neural Networks

Yoshifusa Ito, Yoshifusa Ito, 2008, Neural Computation.

Learning of Mahalanobis Discriminant Functions by a Neural Network

Cidambi Srinivasan, Hiroyuki Izumi, Yoshifusa Ito, 2009, ICONIP.

Approximation of Bayesian Discriminant Function by Neural Networks in Terms of Kullback-Leibler Information

Cidambi Srinivasan, Yoshifusa Ito, C. Srinivasan, 2001, ICANN.

Activation Functions Defined on Higher-Dimensional Spaces for Approximation on Compact Sets with and without Scaling

Yoshifusa Ito, Yoshifusa Ito, 2003, Neural Computation.

Multi-category Bayesian Decision by Neural Networks

Cidambi Srinivasan, Hiroyuki Izumi, Yoshifusa Ito, 2008, ICANN.

Simultaneous learning of several Bayesian and Mahalanobis discriminant functions by a neural network with additional nodes

Cidambi Srinivasan, Hiroyuki Izumi, Yoshifusa Ito, 2011, The 2011 International Joint Conference on Neural Networks.

Approximation of functions on a compact set by finite sums of a sigmoid function without scaling

Yoshifusa Ito, Yoshifusa Ito, 1991, Neural Networks.

Multicategory Bayesian Decision Using a Three-Layer Neural Network

Cidambi Srinivasan, Yoshifusa Ito, 2003, ICANN.

Surface-tracing approximation by basis functions and its applications to neural networks

Yoshifusa Ito, Yoshifusa Ito, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

Simultaneous Learning of Several Bayesian Discriminant Functions by a Neural Network with Additional Nodes

Cidambi Srinivasan, Hiroyuki Izumi, Yoshifusa Ito, 2010, Aust. J. Intell. Inf. Process. Syst..

Representation of functions by superpositions of a step or sigmoid function and their applications to neural network theory

Yoshifusa Ito, Yoshifusa Ito, 1991, Neural Networks.

Approximation Capability of Layered Neural Networks with Sigmoid Units on Two Layers

Yoshifusa Ito, Yoshifusa Ito, 1994, Neural Computation.

Superposition of distinguishable point processes

Yoshifusa Ito, 1977 .

Superposition and decomposition of stationary point processes

Yoshifusa Ito, Yoshifusa Ito, 1978, Journal of Applied Probability.

Simultaneous L/sup p/-approximations of polynomials and derivatives on the whole space

Yoshifusa Ito, 1999 .

Generalized Poisson Functionals

Yoshifusa Ito, 1988 .

Computers and Simulation

Søren Asmussen, Alan G. Konheim, Guy Latouche, 1984 .