 | 2008 |
|---|
| 18 |       | Yoshifusa Ito,
Cidambi Srinivasan,
Hiroyuki Izumi:
Multi-category Bayesian Decision by Neural Networks.
ICANN (1) 2008: 21-30 |
| 17 | | Yoshifusa Ito:
Simultaneous Approximations of Polynomials and Derivatives and Their Applications to Neural Networks.
Neural Computation 20(11): 2757-2791 (2008) |
| 2007 |
|---|
| 16 | | Yoshifusa Ito,
Cidambi Srinivasan,
Hiroyuki Izumi:
Learning of Bayesian Discriminant Functions by a Layered Neural Network.
ICONIP (1) 2007: 238-247 |
| 15 | | Yoshifusa Ito,
Crinivasan Srinivasan,
Hiroyuki Izumi:
A Neural Network having Fewer Inner Constants to be Trained and Bayesian Decision.
IJCNN 2007: 2993-2998 |
| 2006 |
|---|
| 14 | | Yoshifusa Ito,
Cidambi Srinivasan,
Hiroyuki Izumi:
Discriminant Analysis by a Neural Network with Mahalanobis Distance.
ICANN (2) 2006: 350-360 |
| 2005 |
|---|
| 13 | | Yoshifusa Ito,
Cidambi Srinivasan,
Hiroyuki Izumi:
Bayesian Learning of Neural Networks Adapted to Changes of Prior Probabilities.
ICANN (2) 2005: 253-259 |
| 12 | | Yoshifusa Ito,
Cidambi Srinivasan:
Bayesian decision theory on three-layer neural networks.
Neurocomputing 63: 209-228 (2005) |
| 2003 |
|---|
| 11 | | Yoshifusa Ito,
Cidambi Srinivasan:
Multicategory Bayesian Decision Using a Three-Layer Neural Network.
ICANN 2003: 253-261 |
| 10 | | Yoshifusa Ito:
Activation Functions Defined on Higher-Dimensional Spaces for Approximation on Compact Sets with and without Scaling.
Neural Computation 15(9): 2199-2226 (2003) |
| 2002 |
|---|
| 9 | | Yoshifusa Ito:
A Weak Condition on Linear Independence of Unscaled Shifts of a Function and Finite Mappings by Neural Networks.
ICANN 2002: 337-343 |
| 2001 |
|---|
| 8 | | Yoshifusa Ito,
Cidambi Srinivasan:
Bayesian decision theory on three layered neural networks.
ESANN 2001: 377-382 |
| 7 | | Yoshifusa Ito,
Cidambi Srinivasan:
Approximation of Bayesian Discriminant Function by Neural Networks in Terms of Kullback-Leibler Information.
ICANN 2001: 135-140 |
| 2000 |
|---|
| 6 | | Yoshifusa Ito:
Surface-Tracing Approximation by Basis Functions and Its Application to Neural Networks.
IJCNN (4) 2000: 227-231 |
| 1996 |
|---|
| 5 | | Yoshifusa Ito:
Nonlinearity creates linear independence.
Adv. Comput. Math. 5(1): 189-203 (1996) |
| 1994 |
|---|
| 4 | | Yoshifusa Ito:
Approximation Capability of Layered Neural Networks with Sigmoid Units on Two Layers.
Neural Computation 6(6): 1233-1243 (1994) |
| 1992 |
|---|
| 3 | | Yoshifusa Ito:
Approximation of continuous functions on Rd by linear combinations of shifted rotations of a sigmoid function with and without scaling.
Neural Networks 5(1): 105-115 (1992) |
| 1991 |
|---|
| 2 | | Yoshifusa Ito:
Representation of functions by superpositions of a step or sigmoid function and their applications to neural network theory.
Neural Networks 4(3): 385-394 (1991) |
| 1 | | Yoshifusa Ito:
Approximation of functions on a compact set by finite sums of a sigmoid function without scaling.
Neural Networks 4(6): 817-826 (1991) |
