Book Description
The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.
An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
Customer Reviews:
Great product & service.......2007-09-21
This was my first purchase from amazon and I was totally impressed by the quality of the product and the service! I would buy again from the same seller and recommend others to do the same.
A Very Bad Sequel.......2007-03-09
I have now used this book 3 times for a class. While the 1st edition did a nice job of covering the material in its time, the additions to in the 2nd addition are a disaster. What the book has going for it is that it at least lists the necessary material for such a course in the table of contents. However, all the additional material is poorly explained at best. The problem sets are too few and the ones that are included are generally weak.
I have tried to use this book, but after constant student complaints and my own difficulty with the text, I have finally concluded that the problem lies with the text and not with the users.
I think an indicator of problems was the large number of errors in the first printing; large here is an understatement. Even in later additions, the 4th, the size of the errata is huge. I think this is indicative of the authors' attention to detail and seriousness in preparation. I have found similar errors and ambiguities in the associate Computer Manual.
The bottom line is that this book has seen its final appearance in our curriculum. I would use any other text, even an older one.
There is simply not enough room or time to point out all the problems with this text. Do yourself a favor if considering this text for a class. Don't bother.
The best book for the discussed field.......2007-02-05
The discussed book is very explanatory and could be students' material for academic lessons.
great book.......2007-01-16
easy to read for computer scientists who are not necessarily experts in statistics. the code in matlab is very good, and helps a lot.
this book is a good introduction to machine learning.
Very well written.......2006-02-26
I liked this book because it does a great job explaining the concepts and the reasoning behind the mathematical formulae. Other books such as "The Elements of Statistical Learning" toss the Math formulas at you and expect you to figure out the significance or the importance of 'em. The book does not shy away from Math - but does a great job presenting it.
Customer Reviews:
Scottie.......2007-09-26
This is the best book I've found that helps to organize the integration space within the industry. This book has helped to organize my thoughts and communicate with others effectively on how to leverage integration patterns. I highly recommend this book to help obtain a foundational understaning of the integration space.
Excellent patterns book.......2007-08-28
Upon recently changing jobs and focusing on messaging design and architecture, I was steered toward this book by my peers. Without getting into too much detail, before joining my new team, I had never heard of patterns (came from a product support area), much less asynchronous messaging design. Needless to say, this book has been invaluable in my learning process as well as conveying our direction to others.
This book is written in such a way that it is very intuitive. Diagrams help support the concepts and code examples as well.
I would highly recommend this as a must read/reference guide for anyone designing messaging solutions.
Great book for messaging pattern understanding.......2007-08-27
This is a fantastic book if you are looking for patterns to base your messaging designs and architecture around. The way this book goes about explaining some of the asynchronous messaging patterns seemed to provide a great deal of benefit to developers and designers who were stuck in the synchronous way of doing things. Great explanations and illustrations, would recommend to anyone researching EAI or ESB technologies or just a more structured, efficient way of messaging in general.
Enterprise Application Integration .......2007-07-29
I've been using the patterns in this book for several years now. These patterns help me to focus on the problems my customers need solved rather than what technology to use. This has helped to produce numerous successful systems and these patterns have consequently become the basis for many architecural redesign efforts at my company.
The Bible for Enterprise Application Integration.......2007-07-12
As a developer working on application integration for the last 5 years I am so thrilled about this purchase. Just started out reading and though I feel a little overwhelmed I can so much relate to all the patterns being discussed. Its being tough to digest and register the terminologies but I am sure I will get there as I progress. Definitely the best technical books I have ever purchased and is must have for any one who is involved with application integration !
Average customer rating:
- Excellent book for pattern analysis and classification!
- The book should change its title
- Ok, but too much math destroys the intuition...
- The best Pattern Recognition textbook I know
- Great Insights, but a hard read
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Pattern Recognition and Machine Learning (Information Science and Statistics)
Christopher M. Bishop
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ASIN: 0387310738 |
Book Description
The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.
This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.
Coming soon:
*For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text)
*For instructors, worked solutions to remaining exercises from the Springer web site
*Lecture slides to accompany each chapter
*Data sets available for download
Customer Reviews:
Excellent book for pattern analysis and classification!.......2007-10-01
Excellent book for pattern analysis and classification! It begins with basic data curve fitting, linear classification models and ends with combining models (tree-based models, graphical models, etc.). Contains great number of examples and exercises. Very good introductory for beginners in pattern analysis, excellent companion for academics and researchers.
The book should change its title.......2007-09-25
This book (PRML) should be re-titled as "PRML: a bayesian approach". Yes, bayesian approach is very useful for machine learning, and sometimes the final goal of learning is to maximize some sort of posterior probability. However, if the author is such a huge fun of bayes statistics, please tell perspective readers in a clear way. Emphasize bayes aspects too much really hurt the quality of this book as a general-purpose textbook of machine learning.
For a better textbook of machine learning, I recommend:
1) The elements of statistical learning (perhaps this book a little hard for beginner in this field -- but as least better than PRML -- you can compare their chapters about linear regression to see which one is better).
2) Pattern classification (focus on classification, not regression. Also not very easy -- anyway, machine learning is not an easy field ^_^).
3) Machine Learning (a little old, but great for beginner.)
These three book also mention bayesian statistics, but in a proper way. If you have some experience in machine learning and have engineering-level math background, just choose the 1) or 2). If you are completely a beginner, first take a glance on 3), and then go to 1) or 2).
Finally, if you want a book that discusses machine learning purely from bayesian perspective, PRML is good.
Ok, but too much math destroys the intuition..........2007-09-09
This book is a fairly thorough overview of typical topics employed in a graduate machine learning course. However, from page 5 on, expect to see more equations on each page than paragraphs of text (with most of the remaining text explaining the context of the variables within the equations). Now, for someone such as myself who enjoys mathematics, this is not a problem. However, I would not recommend this book for someone with a mathematics background that is in any way weak. Furthermore, there is a more fundamental problem with the presentation of the material that warrants this book no more than a 3-star rating: the simple intuitiveness of the concepts is completely lost within the mathematics. Instead of explaining what variables represent and leaving it to the reader to figure out what is going on, this book could be made much more approachable by simply stating the intuition behind the equations. Take the sum rule, one of the first theorems in the book, for an example of how the author muddles what is effectively a basic and intuitive concept: the book has a fairly lengthy definition of several variables representing concepts such as "the number of observations in which x_ij appears" prior to presenting a summation over all y-variables (a notational convention that the author admits is "cumbersome" on the next page, and states that "there will be no need for such pedantry" as that which he proceeds to perpetrate throughout the book!), while he could have simply presented the simplified sum on the following page (p(X) = sum(p(X,Y), Y)) and it would be immediately clear to most readers what he was attempting to explain. He could also simply state the intuition behind the theorem in English, that summing over every event yields a probability of one, and therefore summing over all events in which a variable appears effectively marginalizes the variable (something he comes close to doing after the presentation of the equation, but by then, the reader's time has already been wasted). Similar examples abound throughout the book, becoming particularly bad during the middle sections, when the techniques begin to become less intuitive.
As another reader mentioned, the author also commits the serious mistake of using pi for a symbol other than the constant or the product operator, which muddles the equations on a skim and forces the reader to refer back to the variable definitions to determine the context.
Having done work in machine learning's applied cousin, data mining, and thus having used many of the techniques presented in the book in actual research, I can't help but think that the presentation of the book's content could be much clearer. When doing work in the field, we can look up the equations as-needed; it is the knowledge of *when* and *how* to apply or extend these techniques that is more important, and that is the area in which I feel this book is lacking.
The best Pattern Recognition textbook I know.......2007-07-17
This book brings the most updated research in this field. The writing stile combines common-sense intuitive explanations with precise mathematical formulations. A lot of colorful figures support the text and help the reader to understand and absorb the described ideas. Short biographies of scientists like Bayes, Laplace, Gauss etc. (which unfortunately substantially drop after the Ch. 2) provide a brief glancing on humans which are behind these great names. The author makes connections between the different chapters, which help the reader to see a wide picture. But don't expect for an easy work. As every deep scientific text it is sometimes fluent and fun, and sometimes demands an effort, rereading the same text again and again, and referring to other references. Personally I feel a great satisfaction when after such an effort the concept became clear to me.
The other useful feature is solved exercises which are available for download from the authors' web site [..]
The main drawback of this book is a relative small amount of detailed examples. As an experienced educator, I know that "a single good example could worth a thousand explanations". It probably will be not an issue with appearance of the practical companion volume (Bishop and Nabney, 2008). The reference to the future (2008) still un-existed publication is unusual, fresh-thinking, and right idea.
With this book C. Bishop continues his "tradition" of writing deep and important scientific books which was started with the "Neural Networks for Pattern Recognition".
A short comment to the reviewer "lew lwndn123", who is deeply disappointed by the fact that this is a textbook. Yes, it is a textbook, and it is clearly written in the "Book Description". It is unfair to "kill" the book just because you didn't really check what you are going to buy, especially you admit that "as a textbook, this is very good text, and deserves 5 starts". I think it will be a decent step if you will correct your review.
Great Insights, but a hard read.......2007-06-16
This new book by Chris Bishop covers most areas of pattern recognition quite exhaustively. The author is an expert, this is evidenced by the excellent insights he gives into the complex math behind the machine learning algorithms. I have worked for quite some time with neural networks and have had coursework in linear algebra, probability and regression analysis, and found some of the stuff in the book quite illuminating.
But that said, I must point out that the book is very math heavy. Inspite of my considerable background in the area of neural networks and statistics, I still was struggling with the equations. This is certainly not the book that can teach one things from the ground up, and thats why I would give it only 3 stars. I am new to kernels, and I am finding the relevant chapters difficult and confusing. This book wont be very useful if all you want to do is write machine learning code. The intended audience for this book I guess are PhD students/researchers who are working with the math related aspects of machine learning. Undergraduates or people with little exposure to machine learning will have a hard time with this book. But that said, time spent in struggling with the contents of this book will certainly pay-off, not instantly though.
Book Description
This completely revised second edition presents an introduction to statistical pattern recognition. Pattern recognition in general covers a wide range of problems: it is applied to engineering problems, such as character readers and wave form analysis as well as to brain modeling in biology and psychology. Statistical decision and estimation, which are the main subjects of this book, are regarded as fundamental to the study of pattern recognition. This book is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field. Each chapter contains computer projects as well as exercises.
Customer Reviews:
A best book on Statistical Pattern Recognition.......2005-09-13
Multivariate analysis is borrowed to name a NEW subject, Statistical Pattern Recognition (SPR). Many statisticians think it unfair or a shame. In spite of these, it is a good reference book of SPR. :-)
[1] Many contents of this book can be found in any graduate textbook of Multivariate Analysis, for instance, Fisher's linear disciminant, etc.
[2] The book is badly printed. Why not using LaTeX?
[3] Guassian distribution is assumed here and there.
[4] It may be good as a reference book, but definitely not as a textbook.
Standard reference and a classic text but with flaws.......2004-01-20
I do not like to consult this book for the following, quite superficial reason. The book is sloppily produced and proofread
(and the fault is [probably] mainly the publisher's instead of the author's). This manifests itself, e.g., as follows
(1) the typography is flawed (the equations hurt at least my eyes);
(2) at its each appearance, the all-important >
< -sign goes the wrong way.
good coverage for engineers.......2000-08-04
Fukunaga is a standard source for pattern recognition methods often cited in the engineering literature. Covers parametric (particularly linear and quadratic discriminant algorithms) and nonparametric methods (density estimation). It is designed for and popular with engineers. When I was working at Nichols Research Corporation Fukunaga's papers and this book (earlier edition) were often cited as sources to justify the algorithms we used for discrimination problems. In fact Fukunaga had been a consultant to the company (used primarily by the Boston branch of the company where the KENN algorithms were developed). It is a reputable source. I still like Duda and Hart (1972) for good explanations of the fundamental concepts. For statisticians McLachlan's book is now far and away the best source.
Standard Reference in the Field.......2000-04-06
If you are writing a machine learning paper, and need to cite something to support an argument, you can almost always cite Fukunaga. This work is a standard reference in the field. The presentation of most material is very terse, but that is great if you already have a good feel for the material and need to look up some details about some algorithm or technique. There isn't much about neural networks here, but for the rest of the pattern recognition techniques, this is almost always the first place to start. Another strong point for this book is the use of realistic examples, which illustrate many of the statistical techniques.
Book Description
Computer Manual to Accompany Pattern Classification and its associated MATLAB software is an excellent companion to Duda: Pattern Classfication, 2nd ed, (DH&S). The code contains all algorithms described in Duda as well as supporting algorithms for data generation and visualization. The Manual uses the same terminology as the DH&S text and contains step-by-step worked examples, including many of the examples and figures in the textbook.
The Manual is accompanied by software that is available electronically. The software contains all algorithms in DH&S, indexed to the textbook, and uses symbols and notation as close as possible to the textbook. The code is self-annotating so the user can easily navigate, understand and modify the code.
Customer Reviews:
Underwhelmed.......2007-04-04
Talk about over-hype from reviewer #1!
This "manual" is thin on substantive content, with TONS of whitespace & whitepages to stretch it out to ~125pages. The documentation of the code should be available as a PDF with files on MATLAB's file exchange or on the publisher's website. Save yourself some $$.
Excellent toolbox to learn & use........2004-07-09
I was one of the early access recipient of this toolbox and found it extremely useful. It basically has a whole bunch of cleaning and classification algos.
The toolbox also allows one to extend its use with new algorithms, tweaks or to use our dataset. As long as its formatted in the same fashion.
I would strongly recommend this toolbox, if you are looking for additional material, another book worth having is Christopher Bishop's book.
Book Description
Quoted from Robert Miner's Dynamic Trading Analysis Report, Pesavento has been trading for 30 years. Today, he is primarily a day trader. His new book is well focused and organized. The bulk of the book describes a limited number of high probability patterns which coincide with clusters of Fib price projections that provide the short-term trader with high probability and low capital exposure trade set-ups. These trade set-ups are equally valuable for intermediate term traders as well. The short-term set-ups can also be used to enter for an intermediate term position.
Quote from 1997 Supertrader's Almanac, Larry Pesavento presents a very persuasive argument that such patterns not only exist, but that the patterns can be profitably employed when they exhibit both the correct form and the form is in the correct proportions.
Customer Reviews:
Interesting, but not profitable.......2007-06-28
I read this book years ago, the author goes the extra mile to explain fibs and their relationship to the market.
Unfortunately fibs and fib pattern trading tends to create unnecessary bias based solely on ratios and patterns, this approach tends to be more coincidental than probable.
So wonderfully, interesting practical.......2007-06-27
This is the best book on trading I have read in a long time. Great practical application for my 4X and option trading. Easy to understand format and content. Excellent book that every trader should own.
Fibonacci Ratios With Pattern Recognition.......2005-09-13
This book is a jumbo of Fibonacci charts and of little assistance with the application of the Fibonacci ratio.
Good.......2003-09-12
I've read Profitable Patterns and his Diary so far, and would have to say this book is clearly the best of the three. Sadly though, one would have a tough time discerning the difference between the three books .. since they all present the same ideas and the titles of the books could be changed without anyone noticing. Typical Pesavento Gartley, Fib, Cycle stuff.. if this is a set up you use, then this would be worth reading.
Best Fibonacci Book.......2003-03-09
This is some of the best work on Fibonacci I've seen. It is very advanced but if you expend the effort to learn it the patterns are profitable. For example, I use the Gartley "222" Pattern and the Shapiro Iteration regularly. There are many example charts to graphically explain the patterns. Highly Recommended
Book Description
This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software make it an ideal starting point for further study.
Customer Reviews:
More for mathematicians than computer scientist.......2006-09-20
This book introduces the concepts of kernel-based methods and focuses specifically on Support Vector Machines (SVM). It is hard to read and a good background in mathematic is clearly needed. The book has a strong emphasis on SVM starting from the very first line of text. Concepts are well explained, although equations are not clear. The notation doesn't facilitate the reading at all. The book covers linear as well as kernel learning. The kernel trick is well described. It is easy to understand ideas behind SVM while reading the corresponding chapter. Finally a small chapter on SVM applications is proposed. Unfortunately, it only contains typical SVM applications (i.e. standard problems).
I think this book is good if you:
* Have a strong mathematical background
* Work in the specific domain of SVM (or kernel-based methods in general)
* Want to write a research paper about SVM and need the correct notations
However, this book is NOT intended for people who:
* Don't like to read theorems, corollaries and remarks
* Are not interested in reading hundreds of proofs
This is my personal opinion as a computer scientist: this book is definitely written for mathematicians.
A little dry........2006-01-09
The book is a little dry at times. Also, I didn't get a very clear idea of how to select kernel functions, which seems pretty important.
Not even close to an intro..........2004-03-21
Oh Puhleeeezzzzz... How is your vector math??? Remember your linear algebra well? Do you have a background in SVM's? Intuitively able to suck out of thin air the meaning of the Gamma co-efficient as applied to svm's?? You've read all the background papers and remember your formal logic???? No?? too bad..your out of luck..
This book is more aptly titled an Introduction to the Formalisms of SVM's. If your a software engineer trying to implement one of these, forget it.. Be nice if they put that quadratic algorthim psuedocode into something more readable than greek symbology..
If you are trying to build one of these engines, then this book is of absolutely no help, unless you have a background in machine learning and have read all the papers on SVM's. If you can decompose the math into code in your head, then you might find it entertaining... What I don't get is how all the rest of these reviewers can give such "glowing praise" for this book and have it be so completely worthless as an introduction... makes me think some of these are shills..
Bottom line is, if your trying to code a svm, this book will not help. If your trying to understand how to implement a svm, this book will not help. If you are trying to understand how an svm works, this book will not help. If you want to know the mathematical basis for SVM's and like that presentation.. this is the book for you..
Excellent book.......2003-11-19
I just happened to read the reviews on the book on Support vector machines by Nello Cristianini and John Shawe-Taylor. Could not resist adding my own comments about the book. Excellent book. I plan to use the book for the course on "Fundamentals of computer aided engineering" that I teach at the Swiss Federal Institute of Technology, Lausanne (EPFL).
This is it !.......2001-08-31
The book is just great. The appendix on algorithms could have more explanations. Also the application section is a short. It would have been more usuful to take one of these applicaitons and describe it in details. But all in all, the book is excellent.
Book Description
Traditionally an area of study in computer science, string algorithms have, in recent years, become an increasingly important part of biology, particularly genetics. This volume is a comprehensive look at computer algorithms for string processing. In addition to pure computer science, Gusfield adds extensive discussions on biological problems that are cast as string problems and on methods developed to solve them. This text emphasizes the fundamental ideas and techniques central to today's applications. New approaches to this complex material simplify methods that up to now have been for the specialist alone. With over 400 exercises to reinforce the material and develop additional topics, the book is suitable as a text for graduate or advanced undergraduate students in computer science, computational biology, or bio-informatics.
Customer Reviews:
Very in-depth explanations.......2006-07-27
I bought this book not because I have any interest in computational biology but because at that time I had an interest in (and professional need for) extremely fast and efficient ways to search through massive data stores. In this I was not disappointed, having found thorough treatments of how to do exact pattern matching as well as various types of "closest" match searching though very large data sets in minimal time.
While perhaps overly theoretical for a person like myself who has not had extensive schooling, it certainly matched my expectations.
I would recommend this book to anyone who I thought could benefit from it.
nice intersection of computing and biology.......2006-01-03
The text sits at the intersection of computer science and computational biology. It centres around the observation made by the author and others that often in CS, one has to manipulate strings of text, which are just sequences of text. While in computational biology, a recurrent theme is how to deal with sequences of molecules. These might be in a DNA sample or in a protein.
Surprisingly, from this simple observation, Gusfield manages to gather together considerable material. Over the decades, computing has accrued many algorithms for text string processing. The book's merit is in presenting those which are also applicable in bioinfomatics. The level of treatment is sophisticated, from the computing vantage. Enough so that perhaps the typical geneticist might not be able to easily follow the narrative. But a researcher with a strong background in both fields might be able to benefit.
What it says, it says best........2003-08-17
If you haven't read this book, you don't know biological string matching. The book's focus is clearly on string algorithms, but the author gives good biological significance to the problems that each technique solves. I came away from this book understanding the algorithms, but also knowing why the algorithms were valuable.
No, there isn't any real source code here. That should not be a problem - this book aims above the cut&paste programmer. The book in meant for readers who can not only understand the algorithms, but apply them to unique solutions in unique ways.
String matching is far too broad a topic for any one book to cover. The study can include formal language theory, Gibbs sampling and other non-deterministic optimizations, and probability-based techniques like Markov models. The author chose a well bounded region of that huge territory, and covers the region expertly. The reader will soon realize, though, that algorithms from this book work well as pieces of larger computations. The book's chosen limits certainly do not limit its applicability.
By the way, don't let the biological orientation put you off. DNA analysis is just one place where string-matching problems occur. The author motivates algorithms with problems in biology, but the techniques are applicable by anyone that analyzes strings.
Definitive String Algorithms Text.......2003-01-05
If you like definition-theorem-proof-example and exercise books, Gusfield's book is the definitive text for string algorithms. The algorithms are abstracted from their biological applications, and the book would make sense without reading a single page of the biological motivations. Gusfield aims his book at readers who are fluent in basic algorithms and data structures (at the level of Cormen, Leisersohn and Rivest's excellent text). The exercises are wonderfully illustrative, being neither trivial nor impossible.
All of the major exact string algorithms are covered, including Knuth-Morris-Pratt, Boyer-Moore, Aho-Corasick and the focus of the book, suffix trees for the much harder probem of finding all repeated substrings of a given string in linear time. In addition to exact string matching, there are extensive discussions of inexact matching. Even the discussions of widely known topics like dynamic programming for edit distance are insightful; for instance, we find how to easily cut space requirements from quadratic to linear. There is also a short chapter on semi-numerical matching methods, which are also of use in information retrieval applications. Inexact matching is extended to the threshold all-against-all problem, which finds all substrings of a string that match up to a given edit distance threshold. The theoretical development concludes with the much more difficult problem of aligning multiple sequences with ultrametric trees, with applications to phylogenetic alignment for evolutionary trees (an approach that has also been applied to the evolution of natural languages).
Note that there is no discussion of statistical string matching. For that, Durbin, Eddy, Krogh and Mitchison's "Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acides" is a good choice, or for those more interested in language than biology, Manning and Schuetze's "Statistical Natural Language Processing". There is also no information on more structured string matching models such as context-free grammars, as are commonly used to analyze RNA folding or natural language syntax. Luckily, Durbin et al. and Manning and Schuetze also provide excellent coverage of these higher-order models in their books.
This book is not about efficient implementation. If you need to build these algorithms, you'll also need to know how to write efficient code and tune it for your needs. This is an algorithms book, pure and simple.
As a computer scientist, I found the discussions of computational biology to be more enlightening than in other textbooks on similar topics such as Durbin et al., because Gusfield does not assume the reader has any background in cellular biology. Instead, he provides his own clear and gentle introductions illustrated with algorithms, applications, open problems and extensive references. Like most Cambridge University Press books, this one is beautifully typeset and edited.
All about suffix trees.......2001-11-05
Excellent book on String Algorithms. A lot of material. This is not an easy read, though, relatively not difficult for an algorithms and data-structures book.
This is the most complete resource i could find about suffix trees, how to implement them, usages, and algorithms. Actually, when I took this book, I was interested in suffix arrays. Well - this book explains those better than the original paper do.
Many applications to suffix trees are listed, along with comparisons to other algorithms applied to those problems.
If you need to get into string algorithms from computer science perspective - this is a good book to start. If you want to "feel" of the biologists side of the story, than this is not a good choice.
I use this book as a textbook on the subject, and I'm sure I'll be using it as a reference later on.
This book surely is worth its cost (even if you buy it on Amazon...:-)).
Average customer rating:
- No worries.
- the future is already here
- Amazingish...
- A Virtual Thriller With Virtually No Thrills
- fascinating/absolutey worth reading.
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ASIN: 0425198685 |
Amazon.com
The first of William Gibson's usually futuristic novels to be set in the present, Pattern Recognition is a masterful snapshot of modern consumer culture and hipster esoterica. Set in London, Tokyo, and Moscow, Pattern Recognition takes the reader on a tour of a global village inhabited by power-hungry marketeers, industrial saboteurs, high-end hackers, Russian mob bosses, Internet fan-boys, techno archeologists, washed-out spies, cultural documentarians, and our heroine Cayce Pollard--a soothsaying "cool hunter" with an allergy to brand names.
Pollard is among a cult-like group of Internet obsessives that strives to find meaning and patterns within a mysterious collection of video moments, merely called "the footage," let loose onto the Internet by an unknown source. Her hobby and work collide when a megalomaniac client hires her to track down whoever is behind the footage. Cayce's quest will take her in and out of harm's way in a high-stakes game that ultimately coincides with her desire to reconcile her father's disappearance during the September 11 attacks in New York.
Although he forgoes his usual future-think tactics, this is very much a William Gibson novel, more so for fans who realize that Gibson's brilliance lies not in constructing new futures but in using astute observations of present-day cultural flotsam to create those futures. With Pattern Recognition, Gibson skips the extrapolation and focuses his acumen on our confusing contemporary world, using the precocious Pollard to personify and humanize the uncertain anxiety, optimistic hope, and downright fear many feel when looking to the future. The novel is filled with Gibson's lyric descriptions and astute observations of modern life, making it worth the read for both cool hunters and their prey. --Jeremy Pugh
Book Description
The accolades and acclaim are endless for William Gibson's coast-to-coast bestseller. Set in the post-9/11 present, Pattern Recognition is the story of one woman's never-ending search for the now.
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Cayce Pollard (pronounced 'case") is a legend in the field of market research, paid handsomely to recognize cultural and social patterns that corporations can turn into cash. Google her and you find 'coolhunter," and you may see it suggested that she is a 'sensitive" of some kind, a dowserin the world of global marketing. The truth, according to her friends, is that her sensitivity is closer to allergy, a morbid and sometimes violent reactivity to the symbols of the marketplace. Hired by Blue Ant, the world's hippest ad agency, for the sort of high-corporate re-branding she's known for, a more intriguing project emerges when the head of the firm asks her to determine who's producing a mysterious series of video fragments that have gripped the imaginations of people around the world. The source of this footage, carefully concealed, has so far proven untraceable. For Cayce's worryingly brilliant employer, the footage is the most effective piece of guerilla marketing ever devised. For Cayce herself, the footage has a powerful emotional resonance as she attempts to come to terms with the apparent death of her father - a former U.S. security expert with ties to the intelligence community - in the collapse of the World Trade Center. But what if the sense of nascent meaning that she and others perceive in the footage is only an illusion of meaningfulness - in other words, faulty pattern recognition? As Cayce begins her hunt for the mysterious 'maker", she enlists the help of an odd array of characters, including a young Polish sculptor who works in primitive personal computers , an alcoholic ex-NSA guru hidden away in a rusting house-trailer in the British countryside, an attractive Chinese-American hacker-entrepreneur whose loyalty she questions, and her fellow footage-enthusiast Parkaboy, whom she's come to know (but only, so far, virtually) on a web site devoted to discussion of 'the footage". When her rivalry with a fellow worker at the advertising agency ta
Customer Reviews:
No worries........2007-09-14
To be honest the first of Gibson's novels set in present made me a little nervous. Would he be able to keep the hard edge and tech mix that I was so fond of in his early works. No worries, this was his best since virtual light if not one of his best. Well done. think of that...Gideon's Fall: When You Dont Have a Prayer, Only a Miracle Will Do
the future is already here.......2007-09-12
A beautiful capturing of today as science fiction. Extrapolation zero - we're already here.
Amazingish..........2007-09-11
I really did like this book. It was a little boring at first but once you get into it, it is hard to put down.
A Virtual Thriller With Virtually No Thrills.......2007-09-07
The protagonist is interesting, the plot less so. The author knows his stuff where personal communication technology and, to a lesser degree, marketing communications are concerned. But the novel remains a somewhat lukewarm love story in "cyberthriller" clothes.
fascinating/absolutey worth reading........2007-08-17
I had never read a Gibson book, and do not read sci-fi novels. I really enjoyed this book and found it both intellectually engaging and suspenseful. Especially interesting was the language and sentence structure. The language was both modern and beautiful. I have read many modern novels with "hip" use of language but they usually come out rather gritty-sounding, whereas Gibson uses a voice that is both modern in its efficiency and graceful at the same time.
Book Description
Praise for The Three Skills of Top Trading
"Professor Pruden's new book, The Three Skills of Top Trading, is unquestionably the best book on a specific trading method and the necessary attributes for trading that I have read. His logic, understanding of human foibles, and use of the Wyckoff method of trading are broadly referenced, readable, understandable, and entertaining."
- Charles D. Kirkpatrick, II, CMT, coauthor of
Technical Analysis: The Complete Resource for Financial Market Technicians, Editor of the Journal of Technical Analysis, and board member of the Market Technicians Association
"At long last, someone has taken the time and effort to bring the work and insight of Wyckoff to wider public attention-and Hank Pruden has done so masterfully, with great clarity and eloquence. Hank has taken the best of Wyckoff's work, combining it with the essential aspects of trader discipline and psychology, to provide a highly readable and particularly useful guide to trading. MUST READING!"
- Jacob Bernstein, www.trade-futures.com
"Hank Pruden puts all of the elements needed for successful trading into one volume. This book not only belongs on every trader's shelf but should be close enough for continuous reference."
- Martin J. Pring, President, www.Pring.com
"Dr. Pruden has brought together his lifetime of work in developing a modern approach to analyzing and trading the markets built upon classic market analysis from the early part of the twentieth century and topped off with modern-day tenets of behavioral finance and mental state management."
- Thom Hartle, Director of Marketing for CQG, Inc. (www.cqg.com)
"I usually consider a book to be well worth reading if it gives me one paradigm shift. I believe that this book will give the average investor a lot more than just one."
- Van K. Tharp, PhD, President, Van Tharp Institute
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