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- Yoshifusa Ito
- Neural Networks
- 1991

- Yoshifusa Ito
- Neural Networks
- 1991

- Yoshifusa Ito
- Neural Networks
- 1992

- Yoshifusa Ito
- Neural Computation
- 2008

We have constructed one-hidden-layer neural networks capable of approximating polynomials and their derivatives simultaneously. Generally, optimizing neural network parameters to be trained at later steps of the BP training is more difficult than optimizing those to be trained at the first step. Taking into account this fact, we suppressed the number of… (More)

- Yoshifusa Ito
- Neural Computation
- 1994

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

- Yoshifusa Ito
- Adv. Comput. Math.
- 1996

Any number of linearly independent plane waves can be obtained by scaling, shifting and/or rotating a plane wave created from an activation function. We investigate the condition which ensures linear independence of the plane waves. In the case all the three procedures are combined, the linear independence can be proved by ad hoe methods for most of… (More)

- Yoshifusa Ito, Cidambi Srinivasan
- ICANN
- 2003

- Yoshifusa Ito, Cidambi Srinivasan
- ICANN
- 2001

- Yoshifusa Ito, Cidambi Srinivasan
- ESANN
- 2001

We treat the Bayesian decision problem, mainly the twocategory case. A three layered neural network, having a logistic output unit and a small number of hidden layer units, can approximate the a posteriori probability in L-norm, without knowing the type of the probability distribution before learning, if the log ratio of the a posteriori probabilities is a… (More)