Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models (Complex Adaptive Systems) Review

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Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models (Complex Adaptive Systems) ReviewWhat strikes me each time I open this book is Mr Kecman's sense of pedagogy: it is a lesson in the matter. Not only his book delivers the - sometimes complex - techniques in a highly readable manner, but the concepts behind each of the main tools (SVM, NN & FL) he chose to highlight are always brilliantly put in context. One comes out of the reading with more than a set of equations but rather with a clearer picture of the field.
Mr Kecman is - without a doubt - a great teacher.
This effort to deliver a clear message is furthermore underlined through the numerous original figures: if you are like me and feel that a (good) picture speaks more than a thousand words, you will sure appreciate the way the illustrations complement the text and truly help the understanding.
I have read several other books on the subject but if I had to chose one for teaching purposes, this would be the one. I you want to build a better understanding of the field, get this book: it will pay on the long term.Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models (Complex Adaptive Systems) OverviewThis textbook provides a thorough introduction to the field of learningfrom experimental data and soft computing. Support vector machines (SVM) and neuralnetworks (NN) are the mathematical structures, or models, that underlie learning,while fuzzy logic systems (FLS) enable us to embed structured human knowledge intoworkable algorithms. The book assumes that it is not only useful, but necessary, totreat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory andalgorithms are illustrated by practical examples, as well as by problem sets andsimulated experiments. This approach enables the reader to develop SVM, NN, and FLSin addition to understanding them. The book also presents three case studies: onNN-based control, financial time series analysis, and computer graphics. A solutionsmanual and all of the MATLAB programs needed for the simulated experiments areavailable.

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The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data Review

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The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data ReviewThis was one of the few books that included a very clear and extensive treatment of information extraction techniques. There were plenty of diagrams which is great for a visual learner. All the techniques are explained using both plain English and formulas, so that you can pick up the scientific notation with minimal previous knowledge.
Even when the authors plug their own company and research at the end it was moderately useful in illustrating the concepts mentioned in a real world scenario.The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data OverviewText mining tries to solve the crisis of information overload by combining techniques from data mining, machine learning, natural language processing, information retrieval, and knowledge management. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, this book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches. Finally, it explores current real-world, mission-critical applications of text mining and link detection in such varied fields as M&A business intelligence, genomics research and counter-terrorism activities.

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Handbook of Statistical Analysis and Data Mining Applications Review

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Handbook of Statistical Analysis and Data Mining Applications ReviewThe "Handbook of Statistical Analysis & Data Mining Applications" is the finest book I have seen on the subject. It is not only a beautifully crafted book, with numerous color graphs, chart, tables, and screen shots, but the statistical discussion is both clear and comprehensive.
The text does not use only one statistical data mining application to display examples, but provides a rather thorough training in the use of both SAS-Enterprise Miner and STATISTICA Data Miner. A section on SPSS Clementine is also provided, giving comparisons between the various packages. Also employed are STATISTICA's C&RT, CHAID, MARSpline, and other data mining and graphical analytic tools.
The text does not burden the typical data mining researcher with the internals of how the various tools work. It is therefore not steeped in equations. Some are to be found, of course, but the emphasis is on understanding the concepts involved and on how to apply these concepts to real data - which is provided to the reader in terms of data tutorials. Specialized datasets have been prepared by both authors and outside experts in various areas of inquiry ranging from entertainment, financial, engineering, clinical psychology, dentistry, demographics, medical informatics, meteorology, astronomy, and more. Each tutorial is associated with data stored on either the associated CD that comes with the book, or which can be downloaded from a companion web site. Worked out examples of how to use data mining techniques on such data is provided to help the reader gain a solid feel for the data mining enterprise. The final third of the book is devoted to a partial selection of the available tutorials. The two earlier chapters demonstrate how to use data mining software for the analysis of data.
I highly recommend this work to anyone having an interest in data mining. I might also add that the Amazon price of $72.37 is truly excellent for an 864 page academic text, having full color tables and screen shots on some one-third of the pages, plus a CD. A bargain indeed.
Handbook of Statistical Analysis and Data Mining Applications OverviewThe Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions.

Written "By Practitioners for Practitioners"
Non-technical explanations build understanding without jargon and equations
Tutorials in numerous fields of study provide step-by-step instruction on how to use supplied tools to build models using Statistica, SAS and SPSS software
Practical advice from successful real-world implementations
Includes extensive case studies, examples, MS PowerPoint slides and datasets
CD-DVD with valuable fully-working 90-day software included: "Complete Data Miner - QC-Miner - Text Miner" bound with book


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Statistical Machine Translation Review

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Statistical Machine Translation ReviewPhilipp Koehn is a superb lecturer and teacher in the area of statistical machine translation (SMT). I have being living off his lecture notes from the ACL, LSA summer session and Edinburgh for years and eagerly waiting for this book to tie everything together.
Koehn has the ability to take complex statistical concepts and make them comprehensible. And he has an encyclopedic knowledge of the state-of-the-art in SMT. His bibliography alone is worth the price of this book.
This book will be the gold standard in SMT for years to come. I would highly recommend to students and professionals in the field.Statistical Machine Translation OverviewThis introductory text to statistical machine translation (SMT) provides all of the theories and methods needed to build a statistical machine translator, such as Google Language Tools and Babelfish.In general, statistical techniques allow automatic translation systems to be built quickly for any language-pair using only translated texts and generic software. With increasing globalization, statistical machine translation will be central to communication and commerce. Based on courses and tutorials, and classroom-tested globally, it is ideal for instruction or self-study, for advanced undergraduates and graduate students in computer science and/or computational linguistics, and researchers in natural language processing. The companion website provides open-source corpora and tool-kits.

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Introduction to Information Retrieval Review

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Introduction to Information Retrieval ReviewI 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,
-SriIntroduction to Information Retrieval OverviewClass-tested and coherent, this groundbreaking new textbook teaches web-era information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. Written from a computer science perspective by three leading experts in the field, it gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Although originally designed as the primary text for a graduate or advanced undergraduate course in information retrieval, the book will also create a buzz for researchers and professionals alike.

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Foundations of Statistical Natural Language Processing Review

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Foundations of Statistical Natural Language Processing ReviewThis is the best book I've ever read on computational linguistics. It should be ideal for both linguists who want to learn about statistical language processing and those building language applications who want to learn about linguistics. This book isn't even published and it's now my most highly used reference book, joining gems such as Cormen, Leiserson and Rivest's algorithm book, Quirk et al.'s English Grammar, and Andrew Gelman's Bayesian statistics book (three excellent companions to this book, by the way).
The book is written more like a computer science or math book in that it starts absolutely from scratch, but moves quickly and assumes a sophisticated reader. The first one hundred or so pages provide background in probability, information theory and linguistics.
This book covers (almost) every current trend in NLP from a statistical perspective: syntactic tagging, sense disambiguation, parsing, information retrieval, lexical subcategorization, Hidden Markov Models, and probabilistic context-free grammars. It also covers machine translation and information retrieval in later chapters.
It covers all the statistical techniques used in NLP from Bayes' law through to maximum entropy modeling, clustering: nearest neighbors and decision trees, and much more.
What you won't find is information on applications to higher-level discourse and dialogue phenomena like pronoun resolution or speech act classification.Foundations of Statistical Natural Language Processing OverviewStatistical approaches to processing natural language text have becomedominant in recent years. This foundational text is the first comprehensiveintroduction to statistical natural language processing (NLP) to appear. The bookcontains all the theory and algorithms needed for building NLP tools. It providesbroad but rigorous coverage of mathematical and linguistic foundations, as well asdetailed discussion of statistical methods, allowing students and researchers toconstruct their own implementations. The book covers collocation finding, word sensedisambiguation, probabilistic parsing, information retrieval, and otherapplications.

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