- Gebundene Ausgabe: 625 Seiten
- Verlag: Academic Press Inc (Oktober 1998)
- Sprache: Englisch
- ISBN-10: 0126861404
- ISBN-13: 978-0126861402
- Größe und/oder Gewicht: 23,7 x 15,9 x 3,3 cm
- Durchschnittliche Kundenbewertung: 4 Kundenrezensionen
- Amazon Bestseller-Rang: Nr. 2.382.626 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
- Komplettes Inhaltsverzeichnis ansehen
Pattern Recognition (Englisch) Gebundene Ausgabe – Oktober 1998
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"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 ... "Larry O'Gorman, IAPR Newsletter, April 2006 -- Dieser Text bezieht sich auf eine vergriffene oder nicht verfügbare Ausgabe dieses Titels.
Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. This volume's treatment covers pattern recognition applications, from image analysis to speech recognition and communications. It includes discussion of the latest techniques in wavelets, wavelet packets, and fractals. This book presents material on neural networks, and enhances student motivation by approaching pattern recognition from the designer's point of view. A direct result of more than 10 years of teaching experience, the text was developed by the authors through use in their own classrooms.Alle Produktbeschreibungen
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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This book is a formal treatment of pattern recognition that is geared to a readership with a strong mathematical background and which makes as its major theme the difference between `supervised' and `unsupervised' pattern recognition, with this difference sometimes being more qualitative than what one would like. In the introduction to the book the authors make clear the distinction between these approaches, motivate the problem of the classification of features, and outline briefly the stages in the design of a pattern classification system. As is well known, supervised pattern recognition involves the use of training data, whereas unsupervised pattern recognition does not. In the latter case, it is left to the machine to find similarities in the feature vectors, and then cluster the similar feature vectors together. Researchers in the field of pattern recognition have devised an enormous number of algorithms and reasoning patterns to perform both unsupervised and supervised learning, and they have not necessarily developed these approaches in the context of machine intelligence. Thus the book could also be viewed as a mathematical theory of pattern recognition instead of one that is embedded in the field of artificial intelligence. However it is classified it is a useful and important work, and is well worth the time taken to read and study.
One of the most interesting (and esoteric) discussions is found in chapter 15 of the book. One of these concerns algorithms for `competitive learning' wherein representatives are designated and then "compete" with each other after a feature vector X is presented to the algorithm. The "winner" is the representative that is closer to X and the representatives are then updated by moving the winner toward X, with the rest remaining constant or move toward X at a slower rate. The competitive learning algorithm is parametrized by the learning rates of the winner and the losers, and the losers can have different learning rates. The investigator however selects the values of these parameters beforehand, and therefore competitive learning strictly speaking should not be classified as totally unsupervised. To be really unsupervised the competitive learning algorithm would have to make the selection of these parameters and tune them as needed to reach the convergence criterion. The authors do discuss briefly a version of the algorithm where the learning rate is variable, but the rate is still subject to certain constraints. Chapter 15 also contains a brief discussion of the use of genetic algorithms in clustering.
Another topic in the book that is both interesting and important and is still surprisingly unknown by many is that of `independent component analysis'. Independent component analysis (ICA) is a generalization of principal component analysis in that it tries to find a transformation that takes a feature vector into one whose components are mutually independent, instead of merely decorrelated. All of the random variables must be non-Gaussian in order for this technique to work, since the Gaussian case gives back the usual principal component analysis. Independent component analysis is beginning to be applied to many different areas, including finance, risk management, medical imaging, and physics. It remains to see whether it will become a standardized tool in the many mathematical and statistical software packages that exist at the present time. The authors discuss two different ways to perform independent component analysis, one being an approach based on higher order cumulants, and the other, interestingly, on mutual information. In the latter approach, the mutual information between the transformed components is calculated to be the Kullback-Leibler probability distance between the joint probability distribution of the transformed components and the product of the marginal probability densities. This distance is of course zero if the components are statistically independent. The strategy is then to find the transformational matrix that minimizes the mutual information, since this will make the components maximally independent. As the authors point out, the problem with this approach is that the elements of the transformation matrix are hidden in the marginal probability distribution functions of the transformed variables. They then outline an approach that allows them to calculate the mutual information with the assumption that the transformation matrix is unitary.
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.
It covers the field thoroughly, and the material is presented very clearly, both
from the mathematical and the algorithm point of view.It includes superb examples and
computer experiments with which the reader can gain insight to the topics.
Also, it is updated with a lot of recent advances on the Pattern Recognition domain,
as e.g. Semi-supervised learning, combining classifiers, spectral clustering,
nonlinear dimensionality reduction. The presentation of all these advanced material is
very well organized and the reader can follow and understand these
sophisticated mathematically concepts.
It is one of my three best books on the topic,
the other ones are the "Neural Networks" of S. Haykin, and "Pattern Recognition and Machine Learning",
of C. Bishop.
I think all these three books are excellent,
in their own way,
and should not be missed from the bookshelf of anyone that copes with the Pattern Recognition field,
either student or researcher.
However, for the reader interested in developing computer algorithms in the Pattern Recognition area,
the book of Theodoridis and Koutroubas is the superior choice.
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