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Algorithms of the Intelligent Web Paperback – July 8, 2009

ISBN-13: 978-1933988665 ISBN-10: 1933988665 Edition: 1st

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Editorial Reviews

About the Author

Dr. Haralambos (Babis) Marmanis is a pioneer in the adoption of machine learning techniques for industrial solutions, and also a world expert in supply management. He has about twenty years of experience in developing professional software. Currently, he is the director of R&D and chief architect, for expense management solutions, at Emptoris, Inc. Babis holds a Ph.D. in applied mathematics from Brown University, an M.S. degree in theoretical and applied mechanics from the University of Illinois at Urbana-Champaign, and B.S. and M.S. degrees in civil engineering from the Aristotle University of Thessaloniki in Greece. He was the recipient of the Sigma Xi award for innovative research in 2000, and he is the author of numerous publications in peer-reviewed international scientific journals, conferences, and technical periodicals.

Dmitry Babenko is the lead for the data warehouse infrastructure at Emptoris, Inc. He is a software engineer and architect with 13 years of experience in the IT industry. He has designed and built a wide variety of applications and infrastructure frameworks for banking, insurance, supply-chain management, and business intelligence companies. He received a M.S. degree in computer science from Belarussian State University of Informatics and Radioelectronics.

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Product Details

  • Paperback: 368 pages
  • Publisher: Manning Publications; 1 edition (July 8, 2009)
  • Language: English
  • ISBN-10: 1933988665
  • ISBN-13: 978-1933988665
  • Product Dimensions: 7.4 x 0.7 x 9.2 inches
  • Shipping Weight: 1.4 pounds (View shipping rates and policies)
  • Average Customer Review: 4.2 out of 5 stars  See all reviews (16 customer reviews)
  • Amazon Best Sellers Rank: #288,528 in Books (See Top 100 in Books)

More About the Author

I live and work in Massachusetts. I like to write books on topics of practical importance and bring a unique perspective into each one of them. I particularly like dissecting areas where knowledge has grown organically or writing about subjects that are considered hard to comprehend but they shouldn't be.

I have had an extensive career in the industry, where I worked in many fields as a software professional. My academic background is quite diverse and includes a Ph.D. in Applied Mathematics from Brown University, a M.Sc. in Theoretical & Applied Mechanics from the University of Illinois at Urbana-Champaign, and a Diploma in Civil Engineering from Aristotle University of Thessaloniki. You can find my academic works by searching on Google Scholar with my last name.

You can find all my books here, on Amazon, and you can always reach me in the following address: h AT marmanis DOT com

Customer Reviews

I have read the book front-to-back (twice!)
Michael Mimo
It is rear these days to find a book that gives you both, general overview and the detail description of technologies but this particular one does just that.
The author makes liberal use of figures and explains what is being done at a high level first, showing pseudocode before actually showing the Java code.

Most Helpful Customer Reviews

88 of 93 people found the following review helpful By Michael Mimo on June 19, 2009
Format: Paperback
I have always had an interest in AI, machine learning, and data mining but I found the introductory books too mathematical and focused mostly on solving academic problems rather than real-world industrial problems. So, I was curious to see what this book was about.

I have read the book front-to-back (twice!) before I write this report. I started reading the electronic version a couple of months ago and read the paper print again over the weekend. This is the best practical book in machine learning that you can buy today -- period. All the examples are written in Java and all algorithms are explained in plain English. The writing style is superb! The book was written by one author (Marmanis) while the other one (Babenko) contributed in the source code, so there are no gaps in the narrative; it is engaging, pleasant, and fluent. The author leads the reader from the very introductory concepts to some fairly advanced topics. Some of the topics are covered in the book and some are left as an exercise at the end of each chapter (there is a "To Do" section, which was a wonderful idea!). I did not like some of the figures (they were probably made by the authors not an artist) but this was only a minor aesthetic inconvenience.

The book covers four cornerstones of machine learning and intelligence, i.e. intelligent search, recommendations, clustering, and classification. It also covers a subject that today you can find only in the academic literature, i.e. combination techniques. Combination techniques are very powerful and although the author presents the techniques in the context of classifiers, it is clear that the same can be done for ecommendations -- as the Bell Korr team did for the Netflix prize.
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Format: Paperback
This is a book that is for the working professional who already knows Java and wants to not only implement intelligent algorithms, he/she wants to understand the theory behind it. All of the code is in Java, so if you don't know this language this book will be over your head. It would also help if you have some background in algorithms along the lines of the material covered in Introduction to Algorithms.

The author is attempting to teach both the algorithms behind the information retrieval that is done on the web and at the same time show those algorithms implemented in Java in such a way that it is clear to the reader what has been done. This approach can be a tricky middle ground often resulting in books that are confusing from both a textbook and from a cookbook standpoint. Fortunately, the author has done a good job of integrating these two viewpoints into a cohesive whole and the result is a book I can heartily recommend. The author makes liberal use of figures and explains what is being done at a high level first, showing pseudocode before actually showing the Java code. Discussions on the inner workings of the algorithms follow.

Note that use is made of higher level libraries such as Lucene when they are available, because this is a book for professionals after all, and your boss would not be pleased if you reinvented the wheel every time you implemented an algorithm. But, don't worry, the explanation behind the code is there too. Another good book that is language agnostic that makes a good companion to this one is
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45 of 50 people found the following review helpful By Russ Abbott on March 22, 2011
Format: Paperback
I selected this book as the text for a course on the basis of the earlier reviews. They sounded so good. Covers the concepts and includes concrete code that does what the concepts intend. But the book didn't live up to the reviews.

First of all, the code uses BeanShell as a way to run the examples. BeanShell is a neat idea. It's one of a number of languages that move Java closer to being a scripting language. But it's not necessary for the book's purposes. It's a bit of a pain to install, and it takes a while to get used to. In the end it's an unnecessary distraction. It's far simpler to run the examples in eclipse with the "scripts" entered as the body of a main() method.

The preceding is a relatively minor point, but in some ways it illustrates some of the problems I had with the book. It focuses too much on the code. Yes, it's nice to have code that does what one is trying to describe, but code is not a substitute for a good explanation. In many places the book provides inadequate descriptions of the concepts, presumably on the grounds that one can just read the code. But code is not tutorial. Code itself must be commented to be understandable. And code cannot replace a good intuitive description of the important ideas.

Furthermore, the code (and the output) take up too much space in the book. There are pages of output when a few lines would suffice, and there are pages of code when a well-constructed paragraph would do. Pearson's coefficient is a good example. There is approximately a page of code to do the calucuation. There is also half a page of code-level comments--e.g., "The method getAverage is self-explanatory; it calculates the average of the vector that's provided as an argument.
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