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Pattern Recognition
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.
856 pages, Hardcover
First published November 1, 1998
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Displaying 1 - 6 of 6 reviews
May 19, 2019
If you are very comfortable with math and want to cover a wide range of Pattern Recognition and Machine Learning techniques, this is the book for you. For everyone else it will be a chore to read through. Unfortunately the book is bogged down with heavy math notation. For the more advanced algorithms, it is next to impossible to get a feel of what the algorithms are and how they work if you are not already familiar with them. Instead of algorithm design, you get the math representation of what the algorithm tries to acheive, then some proof about optimality, then some proof that nobody asked for, and then some more math notation.
Even if one can take extreme math notation to the chest and keep going, they would still find the book a chore. The first 3-5 chapters are overly verbose, where the author goes on and on about the peripherals without touching on the meat of the subject nearly as much. Also, for a lot of the algorithms in these chapters (particularly in the Linear Classifiers chapter), the author opted to go for the counter-intuitive and inefficient solution simply because the math behind it is easier. I understand that opting to go for an easier route to juggle notation is a sound thing to do, but you are sacrificing usefullness.
Some of the chapters in the book are clearly rushed, like the Context-Dependent Classification chapter and a couple of the Clustering chapters. Where, by the way, the author again goes for verbose description instead of brevity.
Now on the positives. The book is pretty good as a reference, if you know an algorithm and want to refresh your memory on the details.
That’s pretty much it. I don’t recommend it, this was pretty much a waste of my time.
Even if one can take extreme math notation to the chest and keep going, they would still find the book a chore. The first 3-5 chapters are overly verbose, where the author goes on and on about the peripherals without touching on the meat of the subject nearly as much. Also, for a lot of the algorithms in these chapters (particularly in the Linear Classifiers chapter), the author opted to go for the counter-intuitive and inefficient solution simply because the math behind it is easier. I understand that opting to go for an easier route to juggle notation is a sound thing to do, but you are sacrificing usefullness.
Some of the chapters in the book are clearly rushed, like the Context-Dependent Classification chapter and a couple of the Clustering chapters. Where, by the way, the author again goes for verbose description instead of brevity.
Now on the positives. The book is pretty good as a reference, if you know an algorithm and want to refresh your memory on the details.
That’s pretty much it. I don’t recommend it, this was pretty much a waste of my time.
January 2, 2018
I have not read other ML-related books, but I found this one very practical for basics of understanding of ML, Neural Networks, and Pattern Recognitions.
March 5, 2017
If I were to synopsize my experience with this book, it would be "hard to read".
It covers a wide range of topics and you can get an idea of algorithms from all across the Pattern Recognition and Machine Learning spectrum - even though it is a bit outdated and lacking in some concepts (like Neural Networks).
The problem is that it is very taxing to get from "I have an idea what this is about" to "I understand what this is about". Wall-o-texts, cumbersome notation, a lack of algorithm analysis all make for a very difficult read. For some reason proving side features of the algorithms takes precedence over actual description on the algorithms. A little bit of pseudocode would have gone a long way, but alas, the reader is left entirely on their own.
I wouldn't recommend this book, unless someone wants to use it as a reference for the many algorithms it covers.
It covers a wide range of topics and you can get an idea of algorithms from all across the Pattern Recognition and Machine Learning spectrum - even though it is a bit outdated and lacking in some concepts (like Neural Networks).
The problem is that it is very taxing to get from "I have an idea what this is about" to "I understand what this is about". Wall-o-texts, cumbersome notation, a lack of algorithm analysis all make for a very difficult read. For some reason proving side features of the algorithms takes precedence over actual description on the algorithms. A little bit of pseudocode would have gone a long way, but alas, the reader is left entirely on their own.
I wouldn't recommend this book, unless someone wants to use it as a reference for the many algorithms it covers.
Currently reading
January 31, 2013Very intuitive book with nice examples
December 15, 2014
Overall it was decent way to learn about pattern recognition, however I felt some of the concepts were hidden behind a wall of text that did not really add to my understanding.
Displaying 1 - 6 of 6 reviews