By Grégoire Montavon, Geneviève Orr, Klaus-Robert Müller
The assumption for this publication dates again to the NIPS'96 workshop "Tips of the exchange" the place, for the 1st time, a scientific test was once made to make an evaluate and assessment of methods for successfully exploiting neural community strategies. influenced through the good fortune of this assembly, the amount editors have ready the current complete documentation. along with together with chapters built from the workshop contributions, they've got commissioned extra chapters to around out the presentation and whole the assurance of suitable subareas. this useful reference publication is prepared in 5 components, each one such as numerous coherent chapters utilizing constant terminology. The paintings begins with a normal creation and every half opens with an advent by means of the quantity editors. A complete topic index permits easy accessibility to person issues. The booklet is a gold mine not just for pros and researchers within the zone of neural details processing, but additionally for newbies to the sector.
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Extra info for Neural Networks: Tricks of the Trade
S. Weigend. Computing second order derivatives in FeedForward networks: A review. IEEE Transactions on Neural Networks, 1993. To appear. 9. C. Darken and J. E. Moody. Note on learning rate schedules for stochastic optimization. In R. P. Lippmann, J. E. Moody, and D. S. Touretzky, editors, Advances in Neural Information Processing Systems, volume 3, pages 832–838. Morgan Kaufmann, San Mateo, CA, 1991. 10. K. I. Diamantaras and S. Y. Kung. Principal Component Neural Networks. Wiley, New York, 1996.
In Giles, Hanson, and Cowan, editors, Advances in Neural Information Processing Systems, vol. 5, San Mateo, CA, 1993. Morgan Kaufmann. 24. M. Møller. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks, 6:525–533, 1993. 25. M. Møller. Supervised learning on large redundant training sets. International Journal of Neural Systems, 4(1):15–25, 1993. 26. J. E. Moody and C. J. Darken. Fast learning in networks of locally-tuned processing units. Neural Computation, 1:281–294, 1989.
The optimal stopping point in this example would be epoch 205. 1% (by finding the minimum at epoch 205). 1% decrease of the generalization error in this case. Nevertheless, overfitting might sometimes go undetected because the validation set is finite and thus not perfectly representative of the problem. Unfortunately, the above or any other validation error curve is not typical in the sense that all curves share the same qualitative behavior. Other curves might never reach a better minimum than the first, or than, say, the third; the mountains and valleys in the curve can be of very different width, height, and shape.