newseasonhw2015 Hier klicken Jetzt Mitglied werden lagercrantz Cloud Drive Photos UHD TVs Learn More praktisch Siemens Shop Kindle Shop Kindle Sparpaket Autorip
  • 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
+ EUR 3,00 Versandkosten
Gebraucht: Gut | Details
Verkauft von BetterWorldBooksDe
Zustand: Gebraucht: Gut
Kommentar: Versand aus Schottland, Versandzeit 7-21 Tage. Frueheres Bibliotheksbuch. Geringe Abnutzungserscheinungen und minimale Markierungen im Text. 100%ige Kostenrueckerstattung garantiert Mit Ihrem Kauf unterstützen Sie Alphabetisierungsprogramme..
Möchten Sie verkaufen?
Zur Rückseite klappen Zur Vorderseite klappen
Anhören Wird wiedergegeben... Angehalten   Sie hören eine Probe der Audible-Audioausgabe.
Weitere Informationen
Alle 3 Bilder anzeigen

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

5 Kundenrezensionen

Alle Formate und Ausgaben anzeigen Andere Formate und Ausgaben ausblenden
Amazon-Preis Neu ab Gebraucht ab
Gebundene Ausgabe
"Bitte wiederholen"
EUR 165,30
Taschenbuch
"Bitte wiederholen"
EUR 56,71
EUR 53,70 EUR 45,11
54 neu ab EUR 53,70 6 gebraucht ab EUR 45,11

Wird oft zusammen gekauft

  • Neural Networks for Pattern Recognition (Advanced Texts in Econometrics)
  • +
  • Pattern Recognition and Machine Learning (Information Science and Statistics)
Gesamtpreis: EUR 130,71
Die ausgewählten Artikel zusammen kaufen
Jeder kann Kindle Bücher lesen — selbst ohne ein Kindle-Gerät — mit der KOSTENFREIEN Kindle App für Smartphones, Tablets und Computer.



Produktinformation

  • Taschenbuch: 502 Seiten
  • Verlag: Oxford University Press, USA (18. Januar 1996)
  • Sprache: Englisch
  • ISBN-10: 0198538642
  • ISBN-13: 978-0198538646
  • Größe und/oder Gewicht: 23,3 x 2,8 x 15,7 cm
  • Durchschnittliche Kundenbewertung: 5.0 von 5 Sternen  Alle Rezensionen anzeigen (5 Kundenrezensionen)
  • Amazon Bestseller-Rang: Nr. 35.977 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
  • Komplettes Inhaltsverzeichnis ansehen

Mehr über den Autor

Entdecken Sie Bücher, lesen Sie über Autoren und mehr

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

excellent... Bishop is able to achieve a level of depth on these topics which is unparalleled in other neural-net texts... clear and concise mathematical analysis. Bishop's text [] picks up where Duda and Hart left off, and, luckily does so with the same level of clarity and elegance. Neural Networks for Pattern Recognition is an excellent read, and represents a real contribution to the neural-net community. IEEE Transactions on Neural Networks, May 1997 this is an excellent book in the specialised area of statistical pattern recognition with statistical neural nets ... a good starting point for new students in those laboratories where research into statistico-neural pattern recognition is being done ... The examples for the reader at the end of this and every chapter are well chosen and will ensure sales as a course textbook ... this is a first-class book for the researcher in statistical pattern recognition. Times Higher Bishop leads the way through a forest of mathematical minutiae. Readers will emerge with a rigorous statistical grounding in the theory of how to construct and train neural networks in pattern recognition. New Scientist [Bishop] has written a textbook, introducing techniques, relating them to the theory, and explaining their pitfalls. Moreover, a large set of exercises makes it attractive for the teacher to use the book... should be warmly welcomed by the neural network and pattern recognition communities. Bishop can be recommended to students and engineers in computer science. The Computer Journal, Volume 39, No. 6, 1996 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 neural network introduction placed in a pattern recognition context. ...He has written a textbook, introducing techniques, relating them to the theory and explaining their pitfalls. Moreover, a large set of exercises makes it attractive for the teacher to use the book ... should be warmly welcomed by the neural network and pattern recognition communities. Robert P. W. Duin, IAPR Newsletter Vol. 19 No. 2 April 1997 This outstanding book contributes remarkably to a better statistical understanding of artificial neural networks. The superior quality of this book is that it presents a comprehensive self-contained survey of feed-forward networks from the point of view of statistical pattern recognition. Zbl.Math 868

Alle Produktbeschreibungen

Welche anderen Artikel kaufen Kunden, nachdem sie diesen Artikel angesehen haben?


In diesem Buch

(Mehr dazu)
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
Mehr entdecken
Wortanzeiger
Ausgewählte Seiten ansehen
Buchdeckel | Copyright | Inhaltsverzeichnis | Auszug | Stichwortverzeichnis
Hier reinlesen und suchen:

Kundenrezensionen

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

Die hilfreichsten Kundenrezensionen

2 von 2 Kunden fanden die folgende Rezension hilfreich Von Ein Kunde am 8. Juli 1999
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 War diese Rezension für Sie hilfreich? Ja Nein Feedback senden...
Vielen Dank für Ihr Feedback. Wenn diese Rezension unangemessen ist, informieren Sie uns bitte darüber.
Wir konnten Ihre Stimmabgabe leider nicht speichern. Bitte erneut versuchen
2 von 2 Kunden fanden die folgende Rezension hilfreich Von Ein Kunde am 21. Juni 1999
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 War diese Rezension für Sie hilfreich? Ja Nein Feedback senden...
Vielen Dank für Ihr Feedback. Wenn diese Rezension unangemessen ist, informieren Sie uns bitte darüber.
Wir konnten Ihre Stimmabgabe leider nicht speichern. Bitte erneut versuchen
1 von 1 Kunden fanden die folgende Rezension hilfreich Von Ein Kunde am 8. Juni 1996
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 War diese Rezension für Sie hilfreich? Ja Nein Feedback senden...
Vielen Dank für Ihr Feedback. Wenn diese Rezension unangemessen ist, informieren Sie uns bitte darüber.
Wir konnten Ihre Stimmabgabe leider nicht speichern. Bitte erneut versuchen
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. Wenn diese Rezension unangemessen ist, informieren Sie uns bitte darüber.
Wir konnten Ihre Stimmabgabe leider nicht speichern. Bitte erneut versuchen
0 von 1 Kunden fanden die folgende Rezension hilfreich Von Ariel Joel Sandez am 6. April 2000
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. Wenn diese Rezension unangemessen ist, informieren Sie uns bitte darüber.
Wir konnten Ihre Stimmabgabe leider nicht speichern. Bitte erneut versuchen