We derive global H^∞ optimal training algorithms for neural networks. These algorithms guarantee the smallest possible prediction error energy over all possible ...
Jan 1, 1994 · We derive global H∞ optimal training algorithms for neural networks. These algorithms guarantee the smallest possible prediction error ...
PDF | We derive global H 1 optimal training algorithms for neural networks. These algorithms guarantee the smallest possible prediction error energy.
H∞ Optimal Training Algorithms and their Relation to Backpropagation. Part of Advances in Neural Information Processing Systems 7 (NIPS 1994).
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Sep 6, 2018 · Backpropagation is a dynamic programming algorithm which attempts to reduce the total amount of derivatives you need to compute by storing and ...
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Apr 3, 2012 · Part 1 Backpropogation algorithms can help to solve problems where there is a discriminant that can help to separate the positive inputs from ...
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Dec 27, 2023 · Backpropagation is an essential part of modern neural network training, enabling these sophisticated algorithms to learn from training ...
Nov 5, 2015 · Scalability: Backpropagation efficiently computes the gradient of the loss function with respect to the weights of the network. This scalability ...
Feb 9, 2022 · This article introduces and explains gradient descent and backpropagation algorithms. These algorithms facilitate how ANNs learn from datasets.