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Barcode Creation In Visual C#.NET Using Barcode printer for VS .Related: Make UPC-A .NET , Printing EAN-13 VB.NET , Create Code 39 VB.NET Ockham s razor states that unnecessarily complex models should not be preferred to simpler ones a very intuitive principle [544, 844] A neural network (NN) model is described by the network weights Model selection in NNs consists of nding a set of weights that best performs the learning task In this sense, the data, and not just the architecture should be viewed as part of the NN model, since the data is instrumental in nding the best weights Model selection is then viewed as the process of designing an optimal NN architecture as well as the implementation of techniques to make optimal use of the available training data Following from the principle of Ockham s razor is a preference then for both simple NN architectures nd optimized training data Usually, model selection techniques address only the question of which architecture best ts the task Standard error back-propagating NNs are passive learners These networks passively receive information about the problem domain, randomly sampled to form a xed size training set Random sampling is believed to reproduce the density of the true distribution However, more gain can be achieved if the learner is allowed to use current attained knowledge about the problem to guide the acquisition of training examples As passive learner, a NN has no such control over what examples are presented for learning The NN has to rely on the teacher (considering supervised learning) to present informative examples The generalization abilities and convergence time of NNs are greatly in uenced by the training set size and distribution: Literature has shown that to generalize well, the training set must contain enough information to learn the task Here lies one of the problems in model selection: the selection of concise training sets Without prior knowledge about the learning task, it is very di cult to obtain a representative training set Theoretical analysis provides a way to compute worst-case bounds on the number of training examples needed to ensure a speci ed level of generalization A widely used theorem concerns the Vapnik-Chervonenkis (VC) dimension [8, 9, 54, 152, 375, 643] This theorem states that the generalization error, EG , of a learner with VC-dimension, dV C , trained on PT random examples will, with high con dence, be no worse than a limit of order dV C /PT For NN learners, the total number of weights in a one hidden layer network is used as an estimate of the VC-dimension This means that the appropriate number of examples to ensure an EG generalization is approximately the number of weights divided by EG The VC-dimension provides overly pessimistic bounds on the number of training examples, often leading to an overestimation of the required training set size [152, 337, 643, 732, 948] Experimental results have shown that acceptable generalization performances can be obtained with training set sizes much less than that. 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