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Amazon.co.uk
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
Amazon.com
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, Bishop thoroughly covers topics such as density estimation, error functions, parameter optimization algorithms, data pre-processing, and Bayesian methods. All topics are organized 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 math knowledge necessary for an undergraduate science degree.
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Einleitungssatz
The term pattern recognition encompasses a wide range of information processing problems of great practical significance, from speech recognition and the classification of handwritten characters, to fault detection in machinery and medical diagnosis. Lesen Sie die erste Seite
Buchdeckel | Copyright | Inhaltsverzeichnis | Auszug | Stichwortverzeichnis | Rückseite
The term pattern recognition encompasses a wide range of information processing problems of great practical significance, from speech recognition and the classification of handwritten characters, to fault detection in machinery and medical diagnosis. Lesen Sie die erste Seite
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Buchdeckel | Copyright | Inhaltsverzeichnis | Auszug | Stichwortverzeichnis | Rückseite
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1 von 1 Kunden fanden die folgende Rezension hilfreich
Format:Taschenbuch
Rarely do I encounter a book of such technical quality that also is a pleasure to read. Bishop moves through sometimes difficult topics in a clear, well-motivated style that is appropriate as both an introduction and a desktop reference on neural nets. Definitely on the "A list."
Bishop chose to not include discussions on a number of topics that might have diluted his focus on pattern recognition (for example, Hebbian learning and neural net approaches to principal components analysis). I think that these choices greatly strengthened the integrity of his presentation.
I would love to see an updated edition with a discussion of recent results in statistical learning theory, kernel methods and support vector machines.
1 von 1 Kunden fanden die folgende Rezension hilfreich
Format:Taschenbuch
I'd like to agree with previous reviewers. Note that you will need a good mathematical background (especially in statistics) to understand the content. However, the book is completely thorough in developing all the key concepts and really tries to give you insight into the meaning behind the equations. It's style is that of an undergraduate level textbook, but a very well written one. To use neural nets effectively, I think you need to have at least one book like this.
1 von 1 Kunden fanden die folgende Rezension hilfreich
Format:Taschenbuch
Bishop cuts through the hype surrounding neural networks, and
shows how they relate to standard techniques
in statistical pattern recognition. He concentrates on feedforward
and radial basis function networks, which are the ones used most
widely in practice. This book is about as mathematical as
Hertz, Krogh and Palmer ("An Introduction to the Theory of Neural
Computation", 1991), but is probably easier to read, and is
certainly of more use to the practitioner. A real gem!
shows how they relate to standard techniques
in statistical pattern recognition. He concentrates on feedforward
and radial basis function networks, which are the ones used most
widely in practice. This book is about as mathematical as
Hertz, Krogh and Palmer ("An Introduction to the Theory of Neural
Computation", 1991), but is probably easier to read, and is
certainly of more use to the practitioner. A real gem!
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