Titlebook: Neural Networks: Tricks of the Trade; Genevieve B. Orr,Klaus-Robert Müller Book 19981st edition Springer-Verlag Berlin Heidelberg 1998 Art

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Adaptive Regularization in Neural Network Modelings an extended version of the recently presented algorithm [.]. The idea is to minimize an empirical estimate - like the cross-validation estimate - of the generalization error with respect to regularization parameters. This is done by employing a simple iterative gradient descent scheme using virtua

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Square Unit Augmented Radially Extended Multilayer Perceptronswork with values set to the square of the first . inputs, properties reminiscent of higher-order neural networks and radial basis function networks (RBFN) are added to the architecture with little added expense in terms of weight requirements. Of particular interest, this architecture has the abilit

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A Dozen Tricks with Multitask Learningning signals of other . tasks. It does this by learning the extra tasks in parallel with the main task while using a shared representation; what is learned for each task can help other tasks be learned better. This chapter describes a dozen opportunities for applying multitask learning in real probl

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Solving the Ill-Conditioning in Neural Network Learnings is shown to be the result of the structure of the network. Also, the well-known problem that weights between ‘higher’ layers in the network have to settle before ‘lower’ weights can converge is addressed. We present a solution to these problems by modifying the structure of the network through the

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Transformation Invariance in Pattern Recognition — Tangent Distance and Tangent Propagationnd computational resources are unlimited, even trivial algorithms will converge to the optimal solution. However, in the practical case, given limited data and other resources, satisfactory performance requires sophisticated methods to regularize the problem by introducing . knowledge. Invariance of

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Combining Neural Networks and Context-Driven Search for Online, Printed Handwriting Recognition in td urgent attention on usable, practical solutions. We discuss a combination and improvement of classical methods to produce robust recognition of hand-printed English text, for a recognizer shipping in new models of Apple Computer’s Newton MessagePad® and eMate®. Combining an artificial neural netwo

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Neural Network Classification and Prior Class Probabilities - if the number of training examples that correspond to each class varies significantly between the classes, then it may be harder for the network to learn the rarer classes in some cases. Such practical experience does not match theoretical results which show that MLPs approximate Bayesian . proba