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Book Neural Networks for Pattern Recognition (Advanced Texts in Econometrics )

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Neural Networks for Pattern Recognition (Advanced Texts in Econometrics )

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    Available in PDF - DJVU Format | Neural Networks for Pattern Recognition (Advanced Texts in Econometrics ).pdf | Language: ENGLISH
    C.M. Bishop(Author)

    Book details


This 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 modeling 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.

This book provides a solid statistical foundation for neural networks from a pattern-recognition perspective. The focus is on the types of neural nets that are most widely used in practical applications, such as the multi-layer perceptron and radial basis function networks. Rather than trying to cover many different types of neural networks, Christopher Bishop thoroughly covers topics such as density estimation, error functions, parameter optimisation algorithms, data pre-processing and Bayesian methods. All topics are organised well and all mathematical foundations are explained before being applied to neural networks. The text is suitable for a graduate or advanced undergraduate level course on neural networks or for practitioners interested in applying neural networks to real-world problems. The reader is assumed to have the level of mathematical knowledge necessary for an undergraduate science degree. --Jake Bond

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Book details

  • PDF | 502 pages
  • C.M. Bishop(Author)
  • Oxford University Press, USA (18 Jan. 1996)
  • English
  • 6
  • Computing & Internet

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Review Text

  • By Christos Dimitrakakis on 18 June 2004

    This is good and quite clear introduction to the field that tries to give the reader an intuitive overview to the neural networks and pattern recognition in general.This is a good book if you are interested in a conversationalist overview to neural networks. There are sufficient formulas to implement the algorithms, so it is good as a list of commonly used neural architectures and how they work, in a single easy-to-access place.However, the book is quite short and hurriedly goes through many different techniques and algorithms, giving you a brief snapshot of each one. Nice pictures abound and explanations, but the understanding that one may obtained from this book will be only superficial. Since the book does not discuss the foundations behind each technique, most of them appear disjoint and unrelated.Actually, the lack of detail and mathematical rigour can be confusing. The need to explain concepts intuitively is hardly an excuse, since there exist other books that manage to achieve clarity, easy of understanding and mathematical rigour, while they develop concepts with sufficient generality for the student to fully grasp the relation between various methods.From my own viewpoint, supervised neural network learning is just a special case of optimisation (the quantity to be optimised is the neural network parameter) under statistical uncertainty (the cost function to be minimised is only partially defined by a set of data and needs to be estimated).Thus, in addition to this book I also recommend taking a look at Bertseka's "Constrained optimization and Lagrange multiplier methods" and his newer "Nonlinear Pogramming" book. His "Neuro-Dynamic programming" book covers a lot more than just neural networks for pattern recognition. Advanced readers that are also interested in optimal stochastic control and reinforcement learning will find it useful.All in all, recommended for people that simply want to implement some neural network algorithms or for people that want a quick introduction. It is advisable, however, to keep a couple of books on estimation theory and on optimisation theory as an aid to deeper understanding.


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