Computer Science Books

Contrary to other books, this one has none of the hype regarding Internet as a research tool. It points out strengths and weakeness on subject areas and provides useful exercises for practice. Covers all the important areas and tools. It is a must in any librarian, cybrarian and information specialist library.

Learn how people are taught to search and research the Internet effectively if you want the public to visit your website. Simple and easy-to-read and not in technical language; obviously written by people who teach the subject.This is an excellent supplement to articles and guidance material aimed at Internet marketing.

We presently teach the Internet in our InfoPaths: Pathways to Information programme. This is our information literacy programme to both the general public and librarians. The information in this book is presented in a simple and clear manner. We found the summary and screen dumps useful not to mention the step by step guide to doing hands-on exercises. Certainly the authors knew what every information specialist needed! This book has become our "bible" and has been referred to often. Highly recommended.

This book presents an excellent review of the last fifty years of information theory, since the days of Claude Shannon. The material in this book is well-compiled and comprehensive. A "must" for any person interested in the flow of Information Theory over time!

Out of all of the IEEE collections of papers which are published in book format.... I truly believe that this is by far the best. The papers, were specially written for the October 1998 edition of IEEE Transactions on Information Theory for the Golden 50th Anniversery of information theory, a field which was arguably started in 1948 with Claude Shannon's publication of The Mathematical Theory of Communications in the Bell Systems Technical Journal.

This collection of papers is from famous authors such as Sergio Verdu, Costello, Cover, Calderbank, Robert Grey, Neuhoff, Berger, Gibson, Proakis, etc.. Most of the papers are survey papers with phenominal references so you can pursue your interest further. Also, since these are survey papers (meaning "tutorials"), unlike most of the usual papers published in Transactions on IT, these are accessable (understandable) to the average person who does not specialize in digital communications or information theory.

The collection of papers is a true delight, bar none, the best tutorial papers I've ever seen written, anywhere. This book is worth thousands of leisurly hours of reading. But don't worry... if your looking for some deep technical stuff, or deep historical coverage of certain topics... there are also papers in the book which fit that criterion. Something for everybody at any level of expertise.

McLaughlin was the editor of the Trans on Info Theory then, and Verdu was the guest editor for the 50th anniversary special issue. The main content of the book is identical to this issue of the transaction except for pages renumbered. The major difference is the improved paper and printing quality :-)

The issue was an excellent collection of great tutorial papers on various important topics. Obviously Verdu did a superb job as the guest editor, besides one article by himself. Cover and Thomas is an excellent information theory textbook, but the coverage and depth are sometimes limited to be a well organized textbook. This book (or T-IT Oct 1998) fills in many parts where Cover and Thomas left untouched. In addition, each review paper by top researchers of the field comes with relatively exhaustive listing of references.

Minor, but important addition in the book part is the indeces. Name index to the name of authors of literatures cited is of great value.

The most important difference of this book from the Transactions is that it comes with a CD-ROM of all reference entries since the Trans on Info Theory began, and the entries are well organized and fully linked. All is in HTML format except for PDF files for the papers contained in the book. Therefore the CD-ROM does not depend on a particular platform (I work at a Microsoft free environment, so I couldn't comment on the CD-ROM otherwise)

For the value of the papers, and the effort required to compile and maintain the complete list of journal refernces are well worth your money to keep a copy, especially because there aren't many info theory books.

Probability dominates our dicision making. The weak point of Eintein's Relativity is in probability, which is very important in Quantum Mechanics. Through this book, I have learned Information Theory, Bayesian Network, and more. All these are vital for my advance study. In other words this book bring me into a new horizon.

As storage technologies continue to improve and lessen in cost, data mining is becoming an increasingly important activity for all types of enterprises and industries. I have read and reviewed many books in this field, but none have presented the key concepts as well as new research together with examples for their proper use as this book. I highly recommend this book for anyone and everyone interested in this field.

No matter one is a beginner or an expert in the field of data mining,"Information-Statistical Data Mining: Warehouse Integration with Examples of Oracle Basics" is really a great hand book. The book is written in plain English to explain the terminologies and the concept of patterns for data mining --- making it easier for readers to understand the basic and advanced data mining concepts. Also, it shows clearly how data mining theories are realized in software implementation, how the implementation is applied to a variety of data sets in different disciplines, and how one can gain knowledge from the valuable information obtained from the process of data mining. Furthermore, it also discussed the way to interpret and to
evaluate the quality of information resulting from the process of data mining. I particularly like the real world case study examples that help me to understand the data mining principles discussed in the book, and to draw me in further into the field. This book is well organized. I strongly recommend the book!

This book provides a detailed presentation of several important approaches to intelligent data analysis. It has ten chapters, each chapter written by a different technical specialist. The book could well serve as a text for a graduate level course on data analysis. It also works well as a reference. There are many useful illustrations and examples.

The first part of this book is focused on classical statistical issues. Arguably, anyone seeking to perform advanced data analysis should have a working knowledge of this area. It is my personal observation that, unfortunately, many workers do not. This book provides a good way of gaining a broad understanding of statistical methods. My only caveat is that the discussion of naïve Bayesian classifiers could have been more extensive. (The chapter on general Bayesian classifiers is other wise well done.) Naïve Bayesian classifiers have been reasonably successful in machine learning and a more in depth treatment would have been useful.

The later chapters focus on machine learning. They provide useful introductions into: induction, neural networks, fuzzy logic, and stochastic search. These chapters are particularly useful to workers contemplating how to best perform advanced analysis of complex, large, and possibly imprecise data sets. Consequently, someone contemplating data mining or other intelligent data analysis applications should seriously consider acquiring this book.

This is a book by Springer Verlag that came out if 1999. This book introduces a lot of useful statistical tools and has chapters written by statisticians and computer scientists. The editors also contribute. They emphasize useful tools and computer tools. It includes material from the artificial intelligence literature including fuzzy set logic, genetic algorithms and expert systems. There is some discussion of data mining, Bayesian methods and neural networks.

Chapters are written on an elementary level for students and pratictioners of modern data analysis techniques. Written mainly as a text but expanded to cover topics of interest to researchers in statistics and computer science by subject matter experts. The last chapter on Systems and Applications by Xiaohui Liu includes coverage of data quality. Among the references on data quality and outlier detection is the book edited by Wright "Statistical Methods and the Improvement of Data Quality". That book was a collection of papers from a conference held in Oak Ridge Tennessee in 1982. That volume was published by Academic Press in 1983. It is not often sighted in the statistical literature but it did contain a number of interesting papers. I contributed a chapter on influence function methods for outlier detection to the Academic Press book.

Hand has written many books on statistics and especially some excellent texts on classification and pattern recognition. His recent work on data mining was published in 1999 by MIT press, a volume he coauthored with Mannila and Smyth. it is one of teh few data mining texts that is highly regarded by the statistical community. Much of that work in referenced in this book particularly in Chapter 1, the overview chapter on intellegent data analysis that Hand wrote himself.

Resampling methods, generalized linear models, Bayesian methods, time series, multivariate analysis, random effects models and entropy are all covered with nice elementary introductions.

This is a great reference source with over 440 articles and books in the list of references.

This is a book by Springer Verlag that came out if 1999. This book introduces a lot of useful statistical tools and has chapters written by statisticians and computer scientists. The editors also contribute. They emphasize useful tools and computer tools. It includes material from the artificial intelligence literature including fuzzy set logic, genetic algorithms and expert systems. There is some discussion of data mining, Bayesian methods and neural networks.

Chapters are written on an elementary level for students and pratictioners of modern data analysis techniques. Written mainly as a text but expanded to cover topics of interest to researchers in statistics and computer science by subject matter experts. The last chapter on Systems and Applications by Xiaohui Liu includes coverage of data quality. Among the references on data quality and outlier detection is the book edited by Wright "Statistical Methods and the Improvement of Data Quality". That book was a collection of papers from a conference held in Oak Ridge Tennessee in 1982. That volume was published by Academic Press in 1983. It is not often sighted in the statistical literature but it did contain a number of interesting papers. I contributed a chapter on influence function methods for outlier detection to the Academic Press book.

Hand has written many books on statistics and especially some excellent texts on classification and pattern recognition. His recent work on data mining was published in 1999 by MIT press, a volume he coauthored with Mannila and Smyth. it is one of teh few data mining texts that is highly regarded by the statistical community. Much of that work in referenced in this book particularly in Chapter 1, the overview chapter on intellegent data analysis that Hand wrote himself.

Resampling methods, generalized linear models, Bayesian methods, time series, multivariate analysis, random effects models and entropy are all covered with nice elementary introductions.

This is a great reference source with over 440 articles and books in the list of references.

This book provides a remarkably broad view of various AI approaches in practice and their targeted problems. All the math and examples are very short and incomplete but it's obvious that details is not the goal of this book. This book is particularly good to consult when one is trying to pick up the right technologies for her problem. Once she gets the big picture, she can follow the numerous futher readings in each field which are specified after each chapter to deepen the understanding and make the execution of the project concrete.

"Intelligent Systems for Engineers and Scientists" is an absolute must for anyone that has to learn about such systems for practical problem solving.

It gives a quick overview of many of the issues that you might stumble upon and supplies the most common solutions to those issues. I found it extremely valuable for its scope. What it lacks in depth you can find online after reading the book cover-to-cover.

Covers every major technique in artificial intelligence in a clear and practical style. Highly recommended.

The book arrived in a timely fashion and fit the description very well.

This book was a great book. The examples helped me understand communication theory and after I read the examples and the TIPS, I saw the relationship between theory and practice. Good job and a great price!

this book is very helpful in giving the main points of many important communication theories. i really understood how theory and practice relate after reading the tips and the examples. it is well priced too.

This is the second edition of THE book on copulas; there are others, eg Joe, and Drouet and Kotz, but this is the best reference work I know of on this topic. The second edition does not frankly have a huge amount of new content; the most obvious is the very useful set of scatterplots used to illustrate the appearance of copulas. This I think shows up some properties like symmetry better than equations could do. If I had purchased the first edition, I probably wouldn't feel it had been worth upgrading---it depends on how deeply you are `into' this topic. On the other hand it's reassuring that there hasn't been a lot of recent work we didn't know about!

I thoroughly recommend this book, and, if I meet the author, I shall reckon that he owes me a drink.

This is a great introduction to the area for those already possessing good mathematical ability and knowledge of distribution theory. All the seminal theorems and references are in here and the reader would be wise to check them out. Well written and communicated, didn't find any typo's. Enjoyed it. This area is fast growing in the area of mathematical finance.

I just got this book, so I can't comment on the contents yet. However, I feel quite ripped off, because this is the previous edition. The hardcover, which is only $20 more is a new second edition, while the paperback is from 1999.

Having used this book to develop actual applications in the real world, I can heartily recommend it with some authority. Most books that cover this subject either skip over the math entirely, leaving you with a vague concept but no useable skills, or dive head-deep into pages of math, giving the impression that the subject is hopelessly complex. Even when the math is adequately covered, there are often "understood" but unexplained conventions in the notation that can lead you very astray. With the MathCad examples you can work with actual useable systems (in simulated form) as you study, making it possible to crack this hard-shelled nut more easily. And the use of MathCad keeps the author honest - everything has to be explicitly defined using well-known MathCad conventions to work. So if you see it in the book, you can do it in the real world. It is also easy to extend or customize the examples, making it possible to develop actual systems with the examples as a starting point. It is not, however, possible to save this work with the supplied "reader" edition of MathCad.

Caveats? It helps to have a working knowledge of MathCad or a similar tool, but this is not absolutely necessary. A tutorial is supplied. If you are serious about using DSP you should have MathCad, MatLab or a similar tool available. To do all the transformations and algebra by hand would take forever and you would miss some of the most useful tools. You definitely need a PC to get the most from this book.

Overall, highly recommended.

Great book, it's worth twice as much as quoted.

Great book giving a realistic description of how the maths work in real dsp applications. Necessary for every student in either DSP or comms to essentially understand and feel what is described in theory.

Although i'm a newbie in information retrieval field (I'm more of a machine learning, computer vision, timeseries person),
I like the book most for the following two reasons :
(1) detailed explanation into the level of implementation in many cases (data structures//memory size etc..)
(2) good review on practice vs. theory. The authors present diverse attractive theories, and on the other hand, discusses why sometimes just simpler methods are hard to be beaten down by those more complicated methods from their experience in practice.

I like that!

This is my first book about information retrieval, and I think that is perfect! The book cover all the modern topics in the information retrieval field. It's very clear and really simple to understand.
Great book!
Congratulations to the authors!

I am a big fan of the authors 1999 book on Statistical Natural Language Processing, and I and was thrilled when I found this new book online -- just search for "Information Retrieval" on Google.

In these two books, they describe the theory behind a vast toolbox which can be used to construct new tools/products for the Internet. Now I can go back to them when the need arises.

For starters, I appreciate the detailed theoretical explanations of topics that I could not find in other texts, and the references to related work are especially helpful. One of the other books I read was Information Retrieval by Grossman, which is an older book but has a more condensed style compared to this. Grossman's discussion of clustering was more high level and referenced a few more papers that I found useful. That helped increase my interest to read through these chapters in which offer greater detail.

Before I felt like I could place each topic in its appropriate context, I had to spend six months of reading both the books, playing with code and finding s/w packages, searching the research literature, reading papers and other books, and then cycling back to the books. Here's are some suggestions for things I'd like to see:

1. A set of recomended programming tools: in some books on Perl -- such as the chapter "Natural Language Tools" in pages 149-171 in "Advanced Perl Programming" by Simon Cozens (O'Reilly) -- you get a very "quick & dirty" introduction to maybe 20-30% of the concepts in these two books along with ways to implement and play around with them. Although Perl has many natural language processing tools, the Cozens book cuts to the chase, explains which are the best tools, and shows you how to use them. I think knowing such shortcuts aids in learning how to apply and improve on them. The more complex and sophisticated topics, the more likely to make it out into the real world if they are easy to play with.

2. More data/examples on what does/doesn't work with end-users: Numbers, graphs, and charts are all good stuff. I always appreciate it when the authors referenced quantitative comparisons, real-world products, and history of Internet. One of the reasons I had to consult the research literature was to broaden my understanding of quantitative comparisons between different techniques involving end-users, which were typically done in the context of complete systems studies that users could try out.

Thanks,
-Sri

I read a lot of books on Compression, but this is indeed the best one I ever bought. I love this book and read it frequently.

The authors of this well balanced textbook succeed admirably well in teaching the subject to the union of students in math and in cs, and to engineers. The danger with subjects that cut accross fields is that they might appeal to the intersection of audiences involved rather than to the much larger union. The authors seem to be at home with all the types of readers, they realize that the lingo and the aim is different for the different and diverse groups of students. Indeed, the tools of information theory, data compression, and arithmetic coding are widely used in science. While the mathematical parts of the subject is old[Shannon, Kolmogorov..., measurements of information, entropy, channel capacity], the applications are still going strong, with new things coming out at a fast rate right up to the present. So the emphasis in the book on data and image compression is very appropriate. There is even a JPEGtool user's guide in the appendix.

I am a gradute student in computer science and I have read a lot of books on this subject, including 1- Digital Image Compression, by Weidong Kou, 2- The Data Compression Book, by Mark Nelson, 3- JPEG Still Image Data Compression Standard, by Pennebaker. I have also browsed few other books on this subject, but one thing I can say for sure, that this is the best book ever written on the subject of Compression. It explains the information theory and data Compression in the best possible way, with best examples. Once read, you will never forget the algorithms. I just love this book, and read it every once in a while.