Facebook Twitter Pinterest
  • Alle Preisangaben inkl. MwSt.
Auf Lager.
Verkauf und Versand durch Amazon. Geschenkverpackung verfügbar.
Menge:1
Neural Networks for Patte... ist in Ihrem Einkaufwagen hinzugefügt worden
Gebraucht: Gut | Details
Verkauft von FatBrain
Zustand: Gebraucht: Gut
Möchten Sie verkaufen?
Zur Rückseite klappen Zur Vorderseite klappen
Hörprobe Wird gespielt... Angehalten   Sie hören eine Hörprobe des Audible Hörbuch-Downloads.
Mehr erfahren
Alle 3 Bilder anzeigen

Neural Networks for Pattern Recognition (Advanced Texts in Econometrics (Paperback)) (Englisch) Taschenbuch – 18. Januar 1996

5.0 von 5 Sternen 5 Kundenrezensionen

Alle Formate und Ausgaben anzeigen Andere Formate und Ausgaben ausblenden
Preis
Neu ab Gebraucht ab
Gebundene Ausgabe
"Bitte wiederholen"
EUR 1.013,69
Taschenbuch
"Bitte wiederholen"
EUR 53,00
EUR 20,59 EUR 19,53
59 neu ab EUR 20,59 13 gebraucht ab EUR 19,53
click to open popover

Wird oft zusammen gekauft

  • Neural Networks for Pattern Recognition (Advanced Texts in Econometrics (Paperback))
  • +
  • Neural Networks and Learning Machines
  • +
  • Pattern Recognition and Machine Learning (Information Science and Statistics)
Gesamtpreis: EUR 219,54
Die ausgewählten Artikel zusammen kaufen

Es wird kein Kindle Gerät benötigt. Laden Sie eine der kostenlosen Kindle Apps herunter und beginnen Sie, Kindle-Bücher auf Ihrem Smartphone, Tablet und Computer zu lesen.

  • Apple
  • Android
  • Windows Phone

Geben Sie Ihre Mobiltelefonnummer ein, um die kostenfreie App zu beziehen.

Jeder kann Kindle Bücher lesen — selbst ohne ein Kindle-Gerät — mit der KOSTENFREIEN Kindle App für Smartphones, Tablets und Computer.



Produktinformation

Produktbeschreibungen

Amazon.de

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.

Pressestimmen

"Should be in the library of any student, teacher, or researcher with a keen interest in modern statistical methods, a large volume of meaningful data to analyze (including simulations), and a fast workstation with good numerical and graphical capabilities."--Journal of the American Statistical
Association
,."..should be warmly welcomed by the neural network and pattern recognition communities. Bishop can be recommended to students and engineers in computer science."--Computer Journal
"An excellent and rigorous treatment of a number of neural network architectures."--Journal of Mathematical Psychology
"Its sequential organization and end-of-chapter exercises make it an ideal mental gymnasium. The author has eschewed biological metaphor and sweeping statements in favour of welcome mathematical rigour."--Scientific Computing World
"A first-class book for the researcher in statistical pattern recognition."--Times Higher Education Supplement
"Although there has been a plethora of books on neural networks published in the last five years, none has really addressed the subject with the necessary mathematical rigour. Professor Bishop's book is the first textbook to provide a clear and comprehensive treatment of the mathematical principles
underlying the main types of artificial neural networks."--Dr. L. Tarassenko and Professor J.M. Brady, Department of Engineering Science, University of Oxford
"There has been an acute need for authoritative textbooks in neural networks that explain the main ideas clearly and consistently using the basic tools of linear algebra, calculus, and simple probability theory. There have been many attempts to provide such a text, but untilnow, none has succeeded.
This is a serious attempt at providing such an ideal textbook. By concentrating on pattern recognition aspects of neural works, the author is able to treat many important topics in much greater depth. The most important contribution of the book is the solid statistical pattern recognition approach,
a sign of increasing maturity in the field."--Mathematical Reviews
"The following keywords concisely indicate the contents: artificial neural networks, statistical pattern recognition, probability density estimation, single-layer networks, multi-layer perception, radial basis functions, error functions, parameter optimization algorithms, Bayesian techniques, etc.
The book is aimed at researchers and practitioners. It can also be used as the primary text in a course for graduate students (129 graded exercises!)."--Industrial Mathematics



"Should be in the library of any student, teacher, or researcher with a keen interest in modern statistical methods, a large volume of meaningful data to analyze (including simulations), and a fast workstation with good numerical and graphical capabilities."--Journal of the American Statistical
Association
, ..".should be warmly welcomed by the neural network and pattern recognition communities. Bishop can be recommended to students and engineers in computer science."--Computer Journal
"An excellent and rigorous treatment of a number of neural network architectures."--Journal of Mathematical Psychology
"Its sequential organization and end-of-chapter exercises make it an ideal mental gymnasium. The author has eschewed biological metaphor and sweeping statements in favour of welcome mathematical rigour."--Scientific Computing World
"A first-class book for the researcher in statistical pattern recognition."--Times Higher Education Supplement
"Although there has been a plethora of books on neural networks published in the last five years, none has really addressed the subject with the necessary mathematical rigour. Professor Bishop's book is the first textbook to provide a clear and comprehensive treatment of the mathematical principles
underlying the main types of artificial neural networks."--Dr. L. Tarassenko and Professor J.M. Brady, Department of Engineering Science, University of Oxford
"There has been an acute need for authoritative textbooks in neural networks that explain the main ideas clearly and consistently using the basic tools of linear algebra, calculus, and simple probability theory. There havebeen many attempts to provide such a text, but until now, none has succeeded.
This is a serious attempt at providing such an ideal textbook. By concentrating on pattern recognition aspects of neural works, the author is able to treat many important topics in much greater depth. The most important contribution of the book is the solid statistical pattern recognition approach,
a sign of increasing maturity in the field."--Mathematical Reviews
"The following keywords concisely indicate the contents: artificial neural networks, statistical pattern recognition, probability density estimation, single-layer networks, multi-layer perception, radial basis functions, error functions, parameter optimization algorithms, Bayesian techniques, etc.
The book is aimed at researchers and practitioners. It can also be used as the primary text in a course for graduate students (129 graded exercises!)."--Industrial Mathematics


"Should be in the library of any student, teacher, or researcher with a keen interest in modern statistical methods, a large volume of meaningful data to analyze (including simulations), and a fast workstation with good numerical and graphical capabilities."--Journal of the American Statistical Association
.,.".should be warmly welcomed by the neural network and pattern recognition communities. Bishop can be recommended to students and engineers in computer science."--Computer Journal
"An excellent and rigorous treatment of a number of neural network architectures."--Journal of Mathematical Psychology
"Its sequential organization and end-of-chapter exercises make it an ideal mental gymnasium. The author has eschewed biological metaphor and sweeping statements in favour of welcome mathematical rigour."--Scientific Computing World
"A first-class book for the researcher in statistical pattern recognition."--Times Higher Education Supplement
"Although there has been a plethora of books on neural networks published in the last five years, none has really addressed the subject with the necessary mathematical rigour. Professor Bishop's book is the first textbook to provide a clear and comprehensive treatment of the mathematical principles underlying the main types of artificial neural networks."--Dr. L. Tarassenko and Professor J.M. Brady, Department of Engineering Science, University of Oxford
"There has been an acute need for authoritative textbooks in neural networks that explain the main ideas clearly and consistently using the basic tools of linear algebra, calculus, and simple probability theory. There have been many attempts to provide such a text, but untilnow, none has succeeded. This is a serious attempt at providing such an ideal textbook. By concentrating on pattern recognition aspects of neural works, the author is able to treat many important topics in much greater depth. The most important contribution of the book is the solid statistical pattern recognition approach, a sign of increasing maturity in the field."--Mathematical Reviews
"The following keywords concisely indicate the contents: artificial neural networks, statistical pattern recognition, probability density estimation, single-layer networks, multi-layer perception, radial basis functions, error functions, parameter optimization algorithms, Bayesian techniques, etc. The book is aimed at researchers and practitioners. It can also be used as the primary text in a course for graduate students (129 graded exercises!)."--Industrial Mathematics



"Should be in the library of any student, teacher, or researcher with a keen interest in modern statistical methods, a large volume of meaningful data to analyze (including simulations), and a fast workstation with good numerical and graphical capabilities."--Journal of the American Statistical Association


.."..should be warmly welcomed by the neural network and pattern recognition communities. Bishop can be recommended to students and engineers in computer science."--Computer Journal


"An excellent and rigorous treatment of a number of neural network architectures."--Journal of Mathematical Psychology


"Its sequential organization and end-of-chapter exercises make it an ideal mental gymnasium. The author has eschewed biological metaphor and sweeping statements in favour of welcome mathematical rigour."--Scientific Computing World


"A first-class book for the researcher in statistical pattern recognition."--Times Higher Education Supplement


"Although there has been a plethora of books on neural networks published in the last five years, none has really addressed the subject with the necessary mathematical rigour. Professor Bishop's book is the first textbook to provide a clear and comprehensive treatment of the mathematical principles underlying the main types of artificial neural networks."--Dr. L. Tarassenko and Professor J.M. Brady, Department of Engineering Science, University of Oxford


"There has been an acute need for authoritative textbooks in neural networks that explain the main ideas clearly and consistently using the basic tools of linear algebra, calculus, and simple probability theory. There have been many attempts to provide such a text, but until now, none has succeeded. This is a serious attempt at providing such an ideal textbook. By concentrating on pattern recognition aspects of neural works, the author is able to treat many important topics in much greater depth. The most important contribution of the book is the solid statistical pattern recognition approach, a sign of increasing maturity in the field."--Mathematical Reviews


"The following keywords concisely indicate the contents: artificial neural networks, statistical pattern recognition, probability density estimation, single-layer networks, multi-layer perception, radial basis functions, error functions, parameter optimization algorithms, Bayesian techniques, etc. The book is aimed at researchers and practitioners. It can also be used as the primary text in a course for graduate students (129 graded exercises!)."--Industrial Mathematics


Alle Produktbeschreibungen

Kundenrezensionen

5.0 von 5 Sternen
5 Sterne
5
4 Sterne
0
3 Sterne
0
2 Sterne
0
1 Stern
0
Alle 5 Kundenrezensionen anzeigen
Sagen Sie Ihre Meinung zu diesem Artikel

Top-Kundenrezensionen

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.
Kommentar 3 Personen fanden diese Informationen hilfreich. War diese Rezension für Sie hilfreich? Ja Nein Feedback senden...
Vielen Dank für Ihr Feedback.
Wir konnten Ihre Stimmabgabe leider nicht speichern. Bitte erneut versuchen
Missbrauch melden
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.
Kommentar 2 Personen fanden diese Informationen hilfreich. War diese Rezension für Sie hilfreich? Ja Nein Feedback senden...
Vielen Dank für Ihr Feedback.
Wir konnten Ihre Stimmabgabe leider nicht speichern. Bitte erneut versuchen
Missbrauch melden
Format: Taschenbuch Verifizierter Kauf
It has been a long way since 1995, and many new techniques and important developments have taken place in the field of A.I. and more concretely, machine learning. Still, this book has aged very well, for two reasons: first, the fundamental techniques and concepts that every practitioner must understand and be able to make use of, like for example parametric techniques for density estimation (kNN), dimensionality reduction (PCA), mixture models, in addition to, of course, neural networks. Second, this book paves the way for moving on to modern techniques like deep energy models and deep belief networks with its last chapter on bayesian techniques.
The explanations are clear and amenable to read. Properties of and advances based on neural networks are presented in a principled way in the context of statistical pattern recognition. The exercises are wisely chosen to ensure the understanding of the presented results, and under what conditions they were derived.
But this book goes beyond theory, A chapter is devoted to optimization techniques, i.e. what algorithms are used to train neural networks in practice. After reading that chapter and going through the exercises you will have a good understanding of the conjugate gradients and LFGB.
The chapter on how to improve generalization, either by optimizing the structure of the network or by combining multiple classifiers is keep at a intuitive level, yet the concepts are well motivated and the few mathetical details help achieving a solid grasp of why do those ideas work. As in the rest of chapters, it is explained how to carry out it in practice, i.e. how I can proofcheck, if my classifier has become better. By the end of the chapter the reader is familiar with the concept of regularization (weight decay), cross validation and bagging.
Kommentar War diese Rezension für Sie hilfreich? Ja Nein Feedback senden...
Vielen Dank für Ihr Feedback.
Wir konnten Ihre Stimmabgabe leider nicht speichern. Bitte erneut versuchen
Missbrauch melden
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!
Kommentar Eine Person fand diese Informationen hilfreich. War diese Rezension für Sie hilfreich? Ja Nein Feedback senden...
Vielen Dank für Ihr Feedback.
Wir konnten Ihre Stimmabgabe leider nicht speichern. Bitte erneut versuchen
Missbrauch melden
Format: Taschenbuch
A good book if you are looking for learning mathematical teory of Neural Networks or set a parameters of comercial application. Not recommended for beginners
Kommentar War diese Rezension für Sie hilfreich? Ja Nein Feedback senden...
Vielen Dank für Ihr Feedback.
Wir konnten Ihre Stimmabgabe leider nicht speichern. Bitte erneut versuchen
Missbrauch melden