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Pattern Recognition [Print Replica] [Kindle Edition]

Sergios Theodoridis , Konstantinos Koutroumbas
5.0 von 5 Sternen  Alle Rezensionen anzeigen (4 Kundenrezensionen)

Kindle-Preis: EUR 58,39 Inkl. MwSt. und kostenloser drahtloser Lieferung über Amazon Whispernet

  • Print Replica:
    Dieses Kindle-Buch sieht wie ein gedrucktes Buch aus
  • ISBN-10 Print Replica: 0123695317
  • ISBN-13 Print Replica: 978-0123695314
  • Edition: 3
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Produktbeschreibungen

Pressestimmen

"The book is written in a very readable, no-nonsense style. I found that there was just the right amount of text to describe a concept, without extraneous verbiage. The same is true for the mathematics, enough for description, not too much to overwhelm." Larry O'Gorman, IAPR Newsletter, April 2006

Kurzbeschreibung

Pattern recognition is a fast growing area with applications in a widely diverse number of fields such as communications engineering, bioinformatics, data mining, content-based database retrieval, to name but a few. This new edition addresses and keeps pace with the most recent advancements in these and related areas. This new edition: a) covers Data Mining, which was not treated in the previous edition, and is integrated with existing material in the book, b) includes new results on Learning Theory and Support Vector Machines, that are at the forefront of today's research, with a lot of interest both in academia and in applications-oriented communities, c) for the first time treats audio along with image applications since in today's world the most advanced applications are treated in a unified way and d) the subject of classifier combinations is treated, since this is a hot topic currently of interest in the pattern recognition community.

* The latest results on support vector machines including v-SVM's and their geometric interpretation
* Classifier combinations including the Boosting approach
* State-of-the-art material for clustering algorithms tailored for large data sets and/or high dimensional data, as required by applications such as web-mining and bioinformatics
* Coverage of diverse applications such as image analysis, optical character recognition, channel equalization, speech recognition and audio classification

Produktinformation

  • Format: Kindle Edition
  • Dateigröße: 20936 KB
  • Seitenzahl der Print-Ausgabe: 856 Seiten
  • Verlag: Academic Press; Auflage: 3 (7. April 2006)
  • Verkauf durch: Amazon Media EU S.à r.l.
  • Sprache: Englisch
  • ASIN: B005H89O2A
  • Text-to-Speech (Vorlesemodus): Nicht aktiviert
  • X-Ray:
  • Word Wise: Nicht aktiviert
  • : Nicht aktiviert
  • Durchschnittliche Kundenbewertung: 5.0 von 5 Sternen  Alle Rezensionen anzeigen (4 Kundenrezensionen)

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7 von 7 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Lehrbuch und Referenz 19. Dezember 2003
Von Ein Kunde
Format:Gebundene Ausgabe
Das Buch deckt die wesentlichen klassischen Verfahren zur Mustererkennung ab. Beginnend mit dem Bayes-Klassifikator, linearen Methoden behandelt es nichtlineare Klassifikatoren, Merkmalsauswahl und -erzeugung und geht schliesslich ausführlich auf Clusteringverfahren ein. Neuere Verfahren (Kernel-Basierte Verfahren, SVMs) werden nicht behandelt. Das Buch ist sehr gut geschrieben: Verständlich, klar und mathematisch präzise genug, um die Verfahren problemlos selber implementieren zu können. Ich benutze Pattern Recognition vor allem als Refernztext zum Nachschlagen und bin sehr zufrieden damit. Wem der Text nicht gefällt: Alternativen wären Duda, Stork, Hart: "Pattern Classification and Scene Analysis" (gut, aber sehr teuer), oder Fukunaga: "Introduction to Statistical Pattern Recognition" (älteres Buch, gut, aber weniger breit gefächert).
War diese Rezension für Sie hilfreich?
5.0 von 5 Sternen very well written introduction 23. Mai 2008
Von CDR
Format:Gebundene Ausgabe
This book presents a very well-written, rich, and self-contained introduction to the field of pattern recognition. The book has the ability and gives the references to motivate the reader for digging deeper into the topic.

The derivation of some methods are presented, some proofs as well. However, it is a relatively pragmatic text, not a pure, abstract mathematical derivation neither a pure recipes book. It presents useful examples for the explained methods.

Maybe, for a newbie it is not the most easy to read introduction, but things will get clear on a second read. I can definitely recommend this book.
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1 von 2 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Continuing... 23. April 2000
Von "a_a"
Format:Gebundene Ausgabe
I am working on a project and I am constantly getting inspired from this book. This book seems to have the practical power of Rogers(Computer Graphics) writting while keeping the theoretical dichipline. So you can safetly combine algorithms and be sure that you are walking on a correct path, simply buy this book all of you who are fed up with a book fool of formulas and "chatting" without practise it will probably save you from a lot of searching. Thats the end of my review. I think I said enough good things and a little criticism on this book.
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0 von 4 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Good Job... 21. April 2000
Von "a_a"
Format:Gebundene Ausgabe
Nice Job. I was (un)lucky to have these fellows as teachers. What they tell in this book is a refined version of the truth...everything "magic" becomes not "magic" if only you can unravel its mysteries, see the limitations and possibly design sth with better bandwidth and then be honest and help others to see the limitations etc...etc. Well mr.Thodoridis "harem" :-) should be proud of him. But still this book could have been better. This is because because mathematics are just numbers if they do not speak to you. Do not expect to find the magic of "Luenberger" or "Brigham" inside this book. But a honest and up to date investigation of theory refined with practise. Also they could have been more illustrative. For example chapter 2 Page 18 at the end. Actualy the pdf of is "shrinking" by a factor L21/L12 < 1. Then draw the pdf on figure 2_1 with dashed lines and then show that xo is moved to the left. This is what I call that maths are speaking to you... Anyway you can not find a person who is perfect as this would mean a signal with a band equal to 1 with IFT equal to a Dirac line ... simply impossible! :-) Buy this book, but I really sugest that it should be studied in an "academic" enviroment. See the quotes I make is just to state the fact that in these times we are living everything is possible...
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23 von 24 Kunden fanden die folgende Rezension hilfreich
3.0 von 5 Sternen It might be the bible for pattern recognition but ... 5. Juni 2010
Von Abel Brown - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe|Verifizierter Kauf
Although there is a TON of info in this book it's really not that great for learning pattern recognition. It's definitely more of a reference than anything else. You can't really read a section and then sit down at your computer and code it up. There a so many details missing. And the equations are so compact that you spend most your time decoding bad notation. If this book were a piece of software it would suffer from feature bloat. If you need to actually do any real applications using the techniques in this book you should definitely by the MATLAB companion text.
12 von 12 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Great book 23. November 2009
Von Antoin Baker - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe
Probably the best book on Pattern Recognition available. While other "Pattern Classification" books simply bombard the reader with a huge variety of unrelated classifiers, this book actually covers the entire "Pattern Recognition" field, not just the classifiers.

Linear and nonlinear classifiers...check
Optimal Feature Selection...check
Feature Generation...check
Template Matching...check
Five substantial chapters on clustering...check

and so much more.

The book is huge, but worth the read. I also appreciate the fact that it has a DSP/image processing/computer vision bias; sort of like the first edition of Duda and Hart.

Great book.
10 von 10 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Excellent book on pattern recognition 9. Juli 2009
Von Dimitrios Gunopulos - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe
This book is an excellent reference for pattern recognition, machine learning, and data mining. It focuses on the problems of classification and clustering, the two most important general problems in these areas. This book has tremendous breadth and depth in its coverage of these topics; it is clearly the best book available on the topic today.

The new edition is an excellent up-to-date revision of the book. I have especially enjoyed the new coverage provided in several topics, including new viewpoints on Support Vector Machines, and the complete in-depth coverage of new clustering methods.

This is a standout characteristic of this book: the coverage of the topics is solid, deep, and principled throughout. The book is very successful in bringing out the important points in each technique, while containing lots of interesting examples to explain complicated concepts. I believe the section on dimensionality reduction is an excellent exposition on this topic, among the best available, and this is just one example. Combined with a coverage unique in its extend, this makes the book appropriate for use as a reference, as a textbook for upper level undergraduate or graduate classes, and for the practitioner that wants to apply these techniques in practice.

I am a professor in Computer Science. Although pattern recognition is not my main focus, I work in the related fields of data mining and databases. I have used this book for my own research and, very successfully, as teaching material. I would strongly recommend this book to both the academic student and the professional.
7 von 7 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen A good book to learn the subject 25. April 2006
Von L. Wang - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe
I bought this book to teach my students on the subject. I am a professor in computer engineering and PR was not my research focus. However, there are many topics covered in this book, which have become more applicable in our area of research (VLSI design). We found this book easy to use. The algorithms are clearly described and my students could implement them easily by just reading the specific chapters we need. We think this is an excellent book to teach ourselves how to apply various PR algorithms in our domain.
6 von 6 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Pattern Recognition 24. Juni 2006
Von Edward J. Ciaccio - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe
Professor Theodoridis has written an exciting new book on pattern recognition. The topic is sometimes neglected, particularly in the fields of biomedical and electrical engineering, but it is essential to the understanding of signal and image shape on a mathematical basis, including similarities and differences in shape as well as how to extract, recognize, and measure the important components. Professor Theodoridis covers all of the classic steps in pattern recognition in great detail and in a readily understood fashion: sensors and pattern extraction, features extraction and selection, clustering, classification, supervised and unsupervised recognition, and evaluation of the system. Each section is backed up with computer simulation examples so that the reader can gain practical experience while reading the book. The author discusses essential concepts for computer programming of the pattern recognition techniques that are discussed. This work is necessarily mathematical, and therefore will tend to be of greatest interest to advanced students and practicing engineers in a variety of fields. Biomedical engineering is a rapidly expanding field that is key to the improvement of health care quality. There are plenty of biomedical examples including those in the section of the book on computer-aided diagnosis, such as for the detection of cancerous lesions in x-ray mammography. The section on speech recognition will be useful to engineers who are designing turnkey pattern recognition systems that include speech recognition as input and/or for use as a security key. Also included in the work are the most recently developed topics of interest including fuzzy clustering algorithms, and neural networks using genetic and annealing methods. This comprehensive work should prove to be an invaluable tool for the library of design engineers who work with signals and images. I heartily recommend it to all with a basic engineering background.

Edward Ciaccio, PhD

Assoc. Professor of Biomedical Engineering

Columbia University in New York
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