1 |
Murphy, P. M., Aha, D. W., UCI Repository of Machine Learning Databases[Machine Readable Data Repository], Univ. of California, Dept of Information and Computer Science, 1996
|
2 |
Laar, P. V. D., Heskes, T., Pruning Using Parameter and Neuronal Metrics, Neural Computation, 11, 977-993, 1999
DOI
ScienceOn
|
3 |
Hansen, L. K., Pedersen, M. W., Controlled Growth of Cascade Correlation Nets, Proceedings of International Conference on Neural Networks, 797-800, 1994
|
4 |
Larsen, J., Svarer, C., Andersen, L. N., Hansen, L. K., Adaptive Regularization in Neural Network Modeling, Neural Networks: Tricks of the Trade, Lecture Notes in Computer Science, 1524, Germany: Springer-Verlag, 113-132, 1998
|
5 |
Hintz-Madsen, M., Hansen, L. K., Larsen, J., Pedersen, M. W., Larsen, M., 'Neural classifier construction using regularization, pruning and test error estimation,' Neural Networks, 11, 1659-1670, 1998
DOI
ScienceOn
|
6 |
Lee, H., Jee, T., Park, H., Lee, Y., A Hybrid Approach to Complexity Optimization of Neutral Networks, Proceedings of International Conference on Neural Information Processing, 3, 1455-1460, 2001
|
7 |
박혜영, Efficient On-line Learning Algorithms Based on Information Geometry for Stochastic Neural Networks, 연세대학교 박사학위 청구 논문, 2000
|
8 |
Qi, M., Zhang, G. P., An investigation of model selection criteria for neural network time series forecasting, European Journal of Operational Research, 132, 666-680, 2001
DOI
ScienceOn
|
9 |
Krogh, A., Hertz, J. A., A Simple Weight Decay Can Improve Generalization, Advances in Neural Information Processing Systems, 4, 950-957, 1992
|
10 |
Pedersen, M. W., Hansen, L. K., Larsen, J., Pruning with generalization based weight saliencies: (OBD, (OBS, Advances in Neural Information Processing Systems, 8, 521-527, 1996
|
11 |
Amari, S., Natural gradient works efficiently in learning, Neural Computation, 10(2), 251-276, 1998
DOI
ScienceOn
|
12 |
Amari, S., Park, H., Fukumizu, K., Adaptive method of realizing natural gradient learning for multilayer perceptrons, Neural Computation,12(6), 1399-1409, 2000
DOI
|
13 |
Andersen, T., Rimer, M., Martinez, T., Optimal Artificial Neural Network Architecture Selection for Baggin. Proceedings of International Joint Conference on Neural Networks, 2, 790 - 795, 2001
|
14 |
Bishop, C. M., Neural Networks for Pattern Recognition, Oxford University Press, 1995
|
15 |
Park, H., Practical Consideration on Generalization Property of Natural Gradient Learning, Lecture Notes in Computer Science, 2084, 402-409, 2001
|
16 |
Heskes, T., On Natural Learning and Pruning in Multilayered Perceptrons, Neural Computation, 12, 1037-1057, 2000
DOI
ScienceOn
|
17 |
Haykin, S., Neural Networks; A Comprehensive Foundation, Prentice-Hall :Second Edition, Inc., 1999
|
18 |
Reed, R. D., Marks, R. J., Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks, MIT Press, 1999
|
19 |
Ripley, B., Pattern Recognition and Neural Networks, Cambridge: Cambridge University Press, 1996
|