| 1999 |
|---|
| 12 | | Xiaohong Chen,
Halbert White:
Improved Rates and Asymptotic Normality for Nonparametric Neural Network Estimators.
IEEE Transactions on Information Theory 45(2): 682-691 (1999) |
| 1996 |
|---|
| 11 | EE | Mark Plutowski,
Garrison W. Cottrell,
Halbert White:
Experience with selecting exemplars from clean data.
Neural Networks 9(2): 273-294 (1996) |
| 1995 |
|---|
| 10 | | Joseph E. Yukich,
Maxwell B. Stinchcombe,
Halbert White:
Sup-norm approximation bounds for networks through probabilistic methods.
IEEE Transactions on Information Theory 41(4): 1021-1027 (1995) |
| 9 | | William G. Baxt,
Halbert White:
Bootstrapping confidence intervals for clinical input variable effects in a network trained to identify the presence of acute myocardial infarction.
Neural Computation 7(3): 624-638 (1995) |
| 8 | EE | Valentina Corradi,
Halbert White:
Regularized neural networks: some convergence rate results.
Neural Computation 7(6): 1225-1244 (1995) |
| 1993 |
|---|
| 7 | EE | Mark Plutowski,
Garrison W. Cottrell,
Halbert White:
Learning Mackey-Glass from 25 Examples, Plus or Minus 2.
NIPS 1993: 1135-1142 |
| 6 | EE | Mark Plutowski,
Shinichi Sakata,
Halbert White:
Cross-Validation Estimates ISME.
NIPS 1993: 391-398 |
| 5 | EE | Kurt Hornik,
Maxwell B. Stinchcombe,
Halbert White:
Letters to the editor.
Neural Networks 6(1): 3 (1993) |
| 1992 |
|---|
| 4 | EE | A. Ronald Gallant,
Halbert White:
On learning the derivatives of an unknown mapping with multilayer feedforward networks.
Neural Networks 5(1): 129-138 (1992) |
| 1990 |
|---|
| 3 | EE | Halbert White:
Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings.
Neural Networks 3(5): 535-549 (1990) |
| 2 | EE | Kurt Hornik,
Maxwell B. Stinchcombe,
Halbert White:
Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks.
Neural Networks 3(5): 551-560 (1990) |
| 1989 |
|---|
| 1 | EE | Kurt Hornik,
Maxwell B. Stinchcombe,
Halbert White:
Multilayer feedforward networks are universal approximators.
Neural Networks 2(5): 359-366 (1989) |
