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Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition,Christopher M. Bishop

Neural Networks for Pattern Recognition   (Citations: 9324)
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his is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.
Published in 1995.
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    • ...Numerous developments in neural networks have demonstrated that they are good at pattern recognition [2, 22]...

    Jingpeng Liet al. A pattern recognition based intelligent search method and two assignme...

    • ...The keyideaistomodelthescoredensityconditionedontheclass variable k. Modeling class conditional density is the fundamental building block in constructing a Bayes classifier [38]...
    • ...In particular, when pðqjQÞ is Gaussian, it is known as Gaussian Mixture Model (GMM) [38]...
    • ...The second model, P ðCjy; qÞ, can be directly approximated using logistic regression or a multilayer Perceptron with a sigmoid activation function [38]...
    • ...In the machine-learning literature [38], this is known as the mixture of experts and P ðQjqÞ is known as a gater, which plays the role of assigning the most competent expert to a subspace of the problem...
    • ...This is because, consistent with the literature on machine learning [38], the discriminative approach requires much fewer parameters to estimate than the generative one, making the former much more suited to a large number of quality measures of which not all may be relevant...
    • ...Consistent with the machine-learning literature [38], correlation among quality measures, if present, will be automatically taken into consideration...

    Norman Pohet al. A Unified Framework for Biometric Expert Fusion Incorporating Quality ...

    • ...However, one of the reported strengths of a neural network is its ability to perform well on datasets that is not part of the training dataset (Bishop 1995)...

    Amin Tayyebiet al. Hierarchical modeling of urban growth across the conterminous USA: dev...

    • ...In particular, the variables that describe which hidden units could have caused a spike in a translation-invariant neuron are not mutually exclusive, whereas the EM algorithm for mixture models (Bishop, 2004) operates with probabilities of hidden variables that are, although not directly observable, can take only one value at a time with some probability...

    Michael Eickenberget al. Characterizing Responses of Translation-Invariant Neurons to Natural S...

    • ...This basically involves solving a quadratic programming problem, while gradient based training methods for neural network architectures on the other hand suffer from the existence of many local minima (Bishop 1995), (Cherkassky and Mulier 2007), (Fletcher 2000)...

    Werickson F. C. Rochaet al. Chemometric Techniques Applied for Classification and Quantification o...

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