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Pattern Recognition (Englisch) Gebundene Ausgabe – 27. November 2008

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Pressestimmen

"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."- Dimitrios Gunopoulos, University of California, Riverside, USA.

"I cut my pattern recognition teeth on a draft version of Duda and Hart (1973). Over subsequent decades, I consistently did two things: (i) recommended Duda and Hart as the best book available on pattern recognition; and (ii) wanted to write the next best book on this topic.

I stopped (i) when the first edition ofS. Theodoridis andK. Koutroumbas'book appeared, and it supplanted the need for (ii)

It was, and is, the best book that has been written on the subject since Duda and Hart's seminal original text. Buy it - you'll be happy you did." - Jim Bezdek, University of West Florida and Senior Fellow, U. of Melbourne (Australia).

"I consider the fourth edition of the book Pattern Recognition, by S. Theodoridis and K. Koutroumbas as the "Bible of Pattern Recognition"- Simon Haykin, McMaster University, Canada

"I have taught a graduate course on statistical pattern recognition for more than twenty five years during which I have used many books with different levels of satisfaction. Recently, I adopted the book by Theodoridis and Koutroumbas (4th edition) for my graduate course on statistical pattern recognition at University of Maryland. This course is taken by students from electrical engineering, computer science, linguistics and applied mathematics. The comprehensive book by Thedoridis and Koutroumbas covers both traditional and modern topics in statistical pattern recognition in a lucid manner, without compromising rigor. This book elegantly addresses the needs of graduate students from the different disciplines mentioned above. This is the only book that does justice to both supervised and unsupervised (clustering) techniques. Every student, researcher and instructor who is interested in any and all aspects of statistical pattern recognition will find this book extremely satisfying. I recommend it very highly." -Rama Chellappa, University of Maryland

"The book Pattern Recognition, by Profs. Sergios Theodoridis and Konstantinos Koutroumbas, has rapidly become the "bible" for teaching and learning the ins and outs of pattern recognition technology. In my own teaching, I have utilized the material in the first four chapters of the book (from basics to Bayes Decision Theory to Linear Classifiers and finally to Nonlinear Classifiers) in my class on fundamentals of speech recognition and have found the material to be presented in a clear and easily understandable manner, with excellent problems and ideas for projects. My students have all learned the basics of pattern recognition from this book and I highly recommend it to any serious student in this area." -Prof. Lawrence Rabiner"

"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."- Dimitrios Gunopoulos, University of California, Riverside, USA.

"I cut my pattern recognition teeth on a draft version of Duda and Hart (1973). Over subsequent decades, I consistently did two things: (i) recommended Duda and Hart as the best book available on pattern recognition; and (ii) wanted to write the next best book on this topic.

I stopped (i) when the first edition ofS. Theodoridis andK. Koutroumbas'book appeared, and it supplanted the need for (ii)

It was, and is, the best book that has been written on the subject since Duda and Hart's seminal original text. Buy it - you'll be happy you did." - Jim Bezdek, University of West Florida and Senior Fellow, U. of Melbourne (Australia).

"I consider the fourth edition of the book Pattern Recognition, by S. Theodoridis and K. Koutroumbas as the "Bible of Pattern Recognition"- Simon Haykin, McMaster University, Canada

"I have taught a graduate course on statistical pattern recognition for more than twenty five years during which I have used many books with different levels of satisfaction. Recently, I adopted the book by Theodoridis and Koutroumbas (4th edition) for my graduate course on statistical pattern recognition at University of Maryland. This course is taken by students from electrical engineering, computer science, linguistics and applied mathematics. The comprehensive book by Thedoridis and Koutroumbas covers both traditional and modern topics in statistical pattern recognition in a lucid manner, without compromising rigor. This book elegantly addresses the needs of graduate students from the different disciplines mentioned above. This is the only book that does justice to both supervised and unsupervised (clustering) techniques. Every student, researcher and instructor who is interested in any and all aspects of statistical pattern recognition will find this book extremely satisfying. I recommend it very highly." -Rama Chellappa, University of Maryland

"The book Pattern Recognition, by Profs. Sergios Theodoridis and Konstantinos Koutroumbas, has rapidly become the "bible" for teaching and learning the ins and outs of pattern recognition technology. In my own teaching, I have utilized the material in the first four chapters of the book (from basics to Bayes Decision Theory to Linear Classifiers and finally to Nonlinear Classifiers) in my class on fundamentals of speech recognition and have found the material to be presented in a clear and easily understandable manner, with excellent problems and ideas for projects. My students have all learned the basics of pattern recognition from this book and I highly recommend it to any serious student in this area." -Prof. Lawrence Rabiner

"

Synopsis

This book considers classical and current theory and practice, of both supervised and unsupervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback. This title is thoroughly developed to include many more worked examples to give greater understanding of this mathematically oriented subject. Many more diagrams are included in it - now in two color - to provide greater insight through visual presentation. Latest hot topics included to further the reference value of the text including semi-supervised learning, combining clustering algorithms, and relevance feedback. It features an accompanying manual that includes Matlab code of the methods and algorithms in the book, together with solved problems and real-life data sets in medical imaging, remote sensing and audio recognition.

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Von Ein Kunde am 19. Dezember 2003
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).
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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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Von Ein Kunde am 23. April 2000
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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Von Ein Kunde am 21. April 2000
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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Die hilfreichsten Kundenrezensionen auf Amazon.com (beta)

Amazon.com: 4.1 von 5 Sternen 28 Rezensionen
24 von 25 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.
10 von 10 Kunden fanden die folgende Rezension hilfreich
1.0 von 5 Sternen Don't get the kindle edition (unless it gets revised) 11. April 2012
Von T. Olaes - Veröffentlicht auf Amazon.com
Format: Kindle Edition Verifizierter Kauf
***This is not a review on the book itself, but rather the KINDLE EDITION.***

As a person who bought this book as text for a graduate class, it was very hard to distinguish some of the letters in the formulas contained within. Also, some characters don't seem to have been translated properly. Especially misleading was when a subscript was rendered within the kindle cloud reader as a superscript... which gives any equation an entirely different meaning when such a thing is done.

I do not recommend purchasing the Kindle edition of this textbook... stick with good old paper until this gets revised.
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.
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