Book Description
Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory.
The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site.
Customer Reviews:
Good book for computational neuroscience.......2007-01-28
I am a mathematician and economist interested in how human brain works. To me, (so far) this is the best book using equations to describe the overall picture of brain functions. Even though it might not touch in-depth research topics, I am sure it gives anyone interested in neuroscience very solid foundations on which more advance topics are built. (It actually invites me to more in-depth research topics, such as reinforcement learning, reward-punishment system, etc.)
If math is your familiar language (says, system of differential equations and Bayesian probability), and you are interested to know, in technical details, how the brain functions, this book is for you. Then, I think, you can go into research topics of your interests after finishing reading this book.
"Theoretical Neuroscience" Dry but Informative.......2006-03-23
"Theoretical Neuroscience" is an in-depth introduction to modeling of neural systems from the chemical/electrical processes within neurons, up through small networks of neurons. It is a little dry, but provides a wealth of information on modeling the electrophysical and computational properties of neurons.
Good starting point for undergraduate students.......2005-07-05
This book covers a wide range of different and important subjects of this field and provides by this a good overview to students new in neuroscience. On the other hand side, the topics discussed are not described thoroughly, but stay on the surface. This maybe no big problem for undergraduates who try just to understand the basics but certainly this is not satisfactory for more advanced students or researches.
In my opinion, this book blurs the view of the reader by presenting results about experiments and theoretical models side by side in a way that no fair and solid discussion is provided indicating clearly the limitations and problems of current models. By this, one could get the feeling that the presented models are more than tool to analyse data. However, exactly this is not true for most of the models as can be seen by the fact that these models can also be found in other areas than neuroscience with other interpretations.
Theoretical Neurosciences from a Computational Perspective.......2004-06-11
This text will become a standard course book for Graduate Schools in Computational Neurosciences. You need to know advanced engineering mathematics & probability theory to be able to understand this book. Dayan & Abbott model primary visual cortical, MT, LIP, and Motor cortical neurons as single units, but also as populations (clusters) of firing cells. They discuss Bayes Theorem, probability theory as it applies to the brain, and parietal lobe function as well. They derive all the equations associated with these models for the student so that more advanced parts of the book are comprehensible. The book is not meant to be a general Neuroscience book, but rather a course book about neuronal modeling, computational neurobiology, and neural engineering. It serves these three purposes well. In my opinion, this is the best written account of neuron modeling out there for the graduate student and researcher. Methods in Neuronal Modeling by Christof Koch is the other great book on this subject. If you own these two books you should be able to advance in high level neural modelling. There are numerous equations and formulae of interest throughout each chapter in these two volumes. The price of 39.00 USD for the hardcover is really quite a bargain.
Great textbook and reference.......2003-08-16
This book is certainly the most thorough textbook currently available
on many aspects of computational neuroscience. It works very carefully
through the fundamental assumptions and equations underlying large
tracts of contemporary quantitative analysis in neuroscience. It is
an ideal introductory book for those with a quantitative background,
and is destined to become a standard course book in the field.
Average customer rating:
- A New Frontier in Computational Geometry
- A fresh approach on topographic maps
- A highly innovative but quite specialized angle!
|
Faithful Representations and Topographic Maps: From Distortion- to Information-Based Self-Organization
Marc M. Van Hulle
Manufacturer: Wiley-Interscience
ProductGroup: Book
Binding: Hardcover
 Neural Networks
| Artificial Intelligence
| Computer Science
| Computers & Internet
| Subjects
| Books
Communication & Signal Processing
| Circuitry
| Computer Science
| Computers & Internet
| Subjects
| Books
Information Systems
| Software Engineering
| Computer Science
| Computers & Internet
| Subjects
| Books
General
| Software
| Computers & Internet
| Subjects
| Books
General
| Electrical & Electronics
| Engineering
| Professional & Technical
| Subjects
| Books
General
| Mechanical
| Engineering
| Professional & Technical
| Subjects
| Books
Adaptive Control
| Control Engineering
| Mechanical
| Engineering
| Professional & Technical
| Subjects
| Books
Advanced Mechanics
| Aerospace
| Engineering
| Professional & Technical
| Subjects
| Books
Mechanics
| Civil
| Engineering
| Professional & Technical
| Subjects
| Books
Geography
| Earth Sciences
| Professional Science
| Professional & Technical
| Subjects
| Books
General
| Geography
| Earth Sciences
| Science
| Subjects
| Books
Look Inside Science Books
| Trip
| Specialty Stores
| Books
ASIN: 0471345075 |
Book Description
A new perspective on topographic map formation and the advantages of information-based learning The study of topographic map formation provides us with important tools for both biological modeling and statistical data modeling. Faithful Representations and Topographic Maps offers a unified, systematic survey of this rapidly evolving field, focusing on current knowledge and available techniques for topographic map formation. The author presents a cutting-edge, information-based learning strategy for developing equiprobabilistic topographic maps—that is, maps in which all neurons have an equal probability to be active—clearly demonstrating how this approach yields faithful representations and how it can be successfully applied in such areas as density estimation, regression, clustering, and feature extraction. The book begins with the standard approach of distortion-based learning, discussing the commonly used Self-Organizing Map (SOM) algorithm and other algorithms, and pointing out their inadequacy for developing equiprobabilistic maps. It then examines the advantages of information-based learning techniques, and finally introduces a new algorithm for equiprobabilistic topographic map formation using neurons with kernel-based response characteristics. The complete learning algorithms and simulation details are given throughout, along with comparative performance analysis tables and extensive references. Faithful Representations and Topographic Maps is an excellent, eye-opening guide for neural network researchers, industrial scientists involved in data mining, and anyone interested in self-organization and topographic maps.
Customer Reviews:
A New Frontier in Computational Geometry.......2000-08-05
This is the first monograph devoted to an extremely important aspect of how nature organizes the sensory surfaces in the higher vertebrates. As this work points out, all sensory surfaces in mammals exist as two dimensional maps which project (or map) onto the (folded) two dimensional surface of the cerebral cortex. Previous authors have pointed out that in the case of the visual system, the retinal surface projects onto area V1 in the occipital cortex in a manner which approximates a "quasi-conformal" complex logarithmic function. Other authors have demonstrated that the cochlear basilar membrane also maps onto the auditory cortex in a similar quasi-conformal fashion. And most obviously, the mechano-receptors in our skin map onto the their corresponding cortical sensory areas in a manner which preserves local order of the projection. The important thing to note here is that this mapping of one 2-D surface onto another 2-D surface preserves the local orthogonality of the map coordinates (defn of quasi-conformal). (In the case of the retinal coordinates, they are the simple R, theta coordinates of the visual field and, in the case of the aural (auditory) map, the coordinates appear to be sound intensity (loudness) and frequency (pitch)). Van Hulle (Kohonen, et al.) describes, in vivid detail, how several "self-organizing" algorithms can make this mapping possible. THIS IS THE IMPORTANT POINT CONVEYED IN THIS MAGNIFICENT WORK. "The underlying INFORMATION coordinates of the data being processed by these 2-D onto 2-D mappings is what 'organizes' or defines the form of the mapping." The only point which the author does not address, is the global nature of these mappings. If we consider how the edges of these 2-D maps project onto eachother, there are three possible projections. One forms an ordinary torus and, unfolded looks like the raster scan of a TV set. This mapping is what has been assumed to be what would be followed in the neural projections of the retina onto area V1, but admits an indeterminacy in that there are two "normal" directions to approach the target surface. The second type of projection, when unfolded, resembles a Mobius strip (or Klein bottle for a closed surface). The importance of this type of projection is that there is only one way for neurons to project onto this surface which removes the ambiguity of the first mapping. (This surface is said to be non-orientable and of genus one in topological terms.) If this is in fact how nature chooses to wire the retino-topic projection, then she must also admit one singular point for each "patch" of map. (Consider what happens when you "squish" a Mobius strip made out of a paper strip between two flat surfaces. There is always a "fold".) About seven years ago, when the color distribution in the mammalian visual cortex was illucidated, it was found that the projection areas were made up of a "patch work quilt" of surfaces each with a singular point about which a set of color strips was splayed (like a peacock tail). Perhaps this is the necessary singular point for this type of retinotopic map. I would hope that the author, in his next book (or edition of this book) might address the global issues of these fascinating 2-D onto 2-D mappings, both natural and computational. (Just for closure, the third possible type of 2-D onto 2-D projection forms a hyperbolic surface whose physical significance is far from clear.)
If this review makes any sense to you, Please BUY THIS BOOK! I'm sure that you will be absolutely fascinated by it's content. However, be forwarned, it would be most helpful to have read Kohonen's "Self Organizing Maps", SV, 1995 before diving into this work. It is written in a "bottom up" fashion so a careful review of the Preface and Table of Contents will help in planning a reading strategy.
A fresh approach on topographic maps.......2000-03-30
The book presents a fresh approach on Topographic Maps, emphasizing ``equiprobabilistic'' topographic maps in which all representational units participate with the same probability in the representation. However, the text goes far beyond a monograph on this particular type of topographic maps and provides an excellent exposition of the topic of self-organizing map models in general, discussing their biological motivation and explaining in depth their connections with important statistical concepts such as vector quantization, non-parametric regression and density estimation. The practicioner will find detailed performance comparisons and psoudo code listings that tremendously facilitate an implementation of the described methods. The potential of the newly introduced equiprobabilistic topographic maps is amply demonstrated with detailed treatments of a broad range of application topics. I am convinced that this book marks an important contribution to the field of topographic map representations and that it has the potential to become a major reference for many years.
A highly innovative but quite specialized angle!.......2000-03-22
For those familiar with the technology and terminology of Kohonen's self-organizing maps, this book is a highly recommendable asset. The insights on the deficiencies of various previously developed techniques and how to improve them are brilliant. Although the matter is presented in a very bottom-up fashion, it is sometimes hard to keep focusing on the big picture while all the different aspects are in-depth explored. This makes the book sometimes hard to understand. Although it was probably never intended to be easily understandable, this is the reason why I didn't give it the full 5 stars.
Book Description
This volume provides students, researchers and application developers with the knowledge and tools to get the most out of using neural networks and related data modelling techniques to solve pattern recognition problems. Each chapter covers a group of related pattern recognition techniques and includes a range of examples to show how these techniques can be applied to solve practical problems.
Features of particular interest include:
- A NETLAB toolbox which is freely available
- Worked examples, demonstration programs and over 100 graded exercises
- Cutting edge research made accessible for the first time in a highly usable form
- Comprehensive coverage of visualisation methods, Bayesian techniques for neural networks and Gaussian Processes
Although primarily a textbook for teaching undergraduate and postgraduate courses in pattern recognition and neural networks, this book will also be of interest to practitioners and researchers who can use the toolbox to develop application solutions and new models.
"...provides a unique collection of many of the most important pattern recognition algorithms. With its use of compact and easily modified MATLAB scripts, the book is ideally suited to both teaching and research."
Christopher Bishop, Microsoft Research, Cambridge, UK
"...a welcome addition to the literature on neural networks and how to train and use them to solve many of the statistical problems that occur in data analysis and data mining"
Jack Cowan, Mathematics Department, University of Chicago, US
"If you have a pattern recognition problem, you should consider NETLAB; if you use NETLAB you must have this book."
Keith Worden, University of Sheffield, UK
Customer Reviews:
An excellent book too.......2005-03-17
This is actually a must-have book for those who want to study pattern recognition.
excellent tools for implementation of P.R. techniques.......2002-06-25
i first bought the book by Bishop (Neural Network for Pattern Recognition) and anyone who have read it can tell u how excellent that book is. This book has a little bit less theory and more on implementation which is perfect for me. This book include all the topics covered in Bishop and then some. How the book is organized, and how concise, easy to understand the material is at the same amazing level as Bishop's. I believe implementing and practicing things u learn is key to understanding them.. if you just look at how things are implemented, things would suddenly become 10 times clearer for you.. often to your own amazement (that you can actually understand all those stuff). this book is extremely useful even if u dont have matlab (just look up the syntax at mathworks web site), cuz matlab code is straightforward to understand. and the material included is very up to date and cutting edge indeed. i highly highly recommend it.
Book Description
The technology of neural networks has attracted much attention in recent years. Their ability to learn nonlinear relationships is widely appreciated and is utilized in many different types of applications; modelling of dynamic systems, signal processing, and control system design being some of the most common. The theory of neural computing has matured considerably over the last decade and many problems of neural network design, training and evaluation have been resolved. This book provides a comprehensive introduction to the most popular class of neural network, the multilayer perceptron, and shows how it can be used for system identification and control. It aims to provide the reader with a sufficient theoretical background to understand the characteristics of different methods, to be aware of the pit-falls and to make proper decisions in all situations. The subjects treated include: System identification: multilayer perceptrons; how to conduct informative experiments; model structure selection; training methods; model validation; pruning algorithms. Control: direct inverse, internal model, feedforward, optimal and predictive control; feedback linearization and instantaneous-linearization-based controllers. Case studies: prediction of sunspot activity; modelling of a hydraulic actuator; control of a pneumatic servomechanism; water-level control in a conical tank. The book is very application-oriented and gives detailed and pragmatic recommendations that guide the user through the plethora of methods suggested in the literature. Furthermore, it attempts to introduce sound working procedures that can lead to efficient neural network solutions. This will make the book invaluable to the practitioner and as a textbook in courses with a significant hands-on component.
Customer Reviews:
all about the perceptron for control systems.......2007-01-07
Much of the book might already be familiar to a reader involved with neural networks. The authors give a recap of decades of progress into using multilayer networks in a feedback mode. Key sections of the text discuss the weighting of the nodes and how you can realistically compute these weights.
But the emphasis differs from most existing texts on neural networks. Here, the authors explain how you might control a dynamical system that could exhibit pronounced nonlinearities. The book is pitched towards someone from control systems theory. The latter has been highly developed, to handle both linear and nonlinear systems. However, if you consult standard texts on control systems, neural networks rarely (if ever) garner a mention. This book tries to correct that deficiency.
it works!.......2006-01-18
This is an excellent book. It contains theory and essential exaples for system identification and control based on ANN approach. The authors have also a WEB site where additional information and the toolboxes (MATLAB format) can be found.
The programs work an they are easy to understand and run.
I first tried the toolboxes (the user manuals are included with the toolboxes) and then ordered the book. It is worth having both. I highly recommend it, specially if you are a newbie in ANN but need a fast start.
Cheers!
Toolbox for Neural Net System Identification.......2005-09-11
The book is straight forward and useful. However, I would like to see more examples using MATLAB.
Average customer rating:
|
Computational Intelligence in Fault Diagnosis (Advanced Information and Knowledge Processing)
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover
Fuzzy Logic
| Algorithms
| Programming
| Computers & Internet
| Subjects
| Books
General
| Artificial Intelligence
| Computer Science
| Computers & Internet
| Subjects
| Books
Machine Vision
| Artificial Intelligence
| Computer Science
| Computers & Internet
| Subjects
| Books
Neural Networks
| Artificial Intelligence
| Computer Science
| Computers & Internet
| Subjects
| Books
Human Vision & Language Systems
| Artificial Intelligence
| Computer Science
| Computers & Internet
| Subjects
| Books
General
| Computers & Internet
| Subjects
| Books
ASIN: 1846283434 |
Book Description
Presenting the latest developments and research results on fault diagnosis approaches using computational intelligence methodologies, this book opens with a review of the state-of-the-art before focusing on various theoretical aspects of computational intelligence methodologies applied to real-world fault diagnosis problems.
Chapters deal with topics such as fuzzy sets applications to fault diagnosis, neural network based fault diagnosis applications and neuro-fuzzy techniques for fault diagnosis. The last chapter considers the problem of diagnosing large scale complex systems using local agents which, can be implemented using computational intelligence based fault diagnosis techniques. Several case studies are used.
This book presents the most recent concerns and research results in industrial fault diagnosis using intelligent techniques, and will be of interest to application engineers/technologists, graduates and researchers wishing to apply these techniques, as well as build up a foundation for further study.
Amazon.com
This book provides a solid statistical foundation for neural networks from a pattern recognition perspective. The focus is on the types of neural nets that are most widely used in practical applications, such as the multi-layer perceptron and radial basis function networks. Rather than trying to cover many different types of neural networks, Bishop thoroughly covers topics such as density estimation, error functions, parameter optimization algorithms, data pre-processing, and Bayesian methods. All topics are organized well and all mathematical foundations are explained before being applied to neural networks. The text is suitable for a graduate or advanced undergraduate level course on neural networks or for practitioners interested in applying neural networks to real-world problems. The reader is assumed to have the level of math knowledge necessary for an undergraduate science degree.
Book Description
This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book describes techniques for modelling probability density functions, and discusses the properties and relative merits of the multi-layer perceptron and radial basis function network models. It also motivates the use of various forms of error functions, and reviews the principal algorithms for error function minimization. As well as providing a detailed discussion of learning and generalization in neural networks, the book also covers the important topics of data processing, feature extraction, and prior knowledge. The book concludes with an extensive treatment of Bayesian techniques and their applications to neural networks.
Customer Reviews:
Only for an expert.......2006-07-20
Mr Bishop's book is very well written and contains a lot of useful information on neural networks. It is outlined well and progresses in a logical form. If, however, you are looking for a book that gives discussions with concrete examples of neural networks applications or set ups, you will be sorely disappointed. The mathematical treatment is universally generalized with very few specific concrete examples shown. Even the exercises will not serve you well. The term 'graded' is used; however, that simply referes to the description of difficulty. There are no answers to these exercises, so unless you have a teacher or are already firmly familiar with the material, you will not know if you have completed them correctly or not. Even worse, the exercises are in general not written to reinforce concepts in the chapter, but in most cases extend the chapter material into new regions.
In summary, this book should only be purchased by someone already familiar with neural networks and their mathematical basis. Anyone else will be wasting their money.
Fabulous.......2006-04-06
This is the best book I have found for a general study of the of neural networks. I found this particularly useful when looking at how to write my own NN frameworks. The depth of the mathematics allowed me to easily answer questions like: 'what if I replaced function abc with xyz'. I have found other texts failed to show key mathematical derivations, or to explore the subtleties of what the maths imply.
The book covers a plethora of topics from simple gradient descent through second order techniques and conjugate gradient, through to the use of 'bayesian techniques' (basically confidence intervals on network outputs), monte carlo techniques etc. Similarly error functions, non-linearities (sigmoids, softmax etc.) and data preparation are all treated.
The extensive bibliography also provides excellent references for further study, (a whos who of the field, as well as actual titles). My copy is now dog earred from frequent reading.
Sheer pleasure........2004-01-28
If you want a very good, intermediate introduction to pattern classification this book must be on your bookshelf. It even does a very nice job explaining the EM algorithm in a few pages! Basic calculus is all you need to understand the book. A must read.
It makes a difficult topic easy to understand.......2003-09-15
The theories of NN and PR are quite difficult to understand. But this book makes them much easier. The author can explain the concepts without using too much formula. If other authors could follow his step then the life is much easier!
Recomended book to read.......2003-07-22
This is a recommended book to read for people who would like to read about statistics and maths. People with few knowledge about these sciences will find it a bit difficult to read.
Average customer rating:
|
Adaptive Pattern Recognition and Neural Networks
Yoh-Han Pao
Manufacturer: Addison-Wesley Pub (Sd)
ProductGroup: Book
Binding: Hardcover
Neural Networks
| Artificial Intelligence
| Computer Science
| Computers & Internet
| Subjects
| Books
General
| Certification Central
| Computers & Internet
| Subjects
| Books
General
| Computers & Internet
| Subjects
| Books
General
| Operating Systems
| Computers & Internet
| Subjects
| Books
Pattern Recognition
| Algorithms
| Programming
| Computers & Internet
| Subjects
| Books
Engineering
| Professional & Technical
| Subjects
| Books
| Aerospace
| Automotive
| Bioengineering
| Chemical
| Civil
| Computer Technology
| Design
| Economics
| Education
| Electrical & Electronics
| Energy
| General
| Industrial, Manufacturing & Operational Systems
| Management
| Marine
| Materials
| Materials Science
| Mechanical
| Nuclear
| Patents & Inventions
| Petroleum, Mining & Geological
| Power Systems
| Reference
| Research
| Special Topics
| Telecommunications
| Welding
Engineering
| Specialty Stores
| Books
| Aerospace
| Automotive
| Bioengineering
| Chemical
| Civil
| Computer Technology
| Design
| Economics
| Education
| Electrical & Electronics
| Energy
| General
| Industrial, Manufacturing & Operational Systems
| Management
| Materials
| Materials Science
| Mechanical
| Nuclear
| Patents & Inventions
| Petroleum, Mining & Geological
| Power Systems
| Reference
| Research
| Special Topics
| Telecommunications
| Welding
ASIN: 0201125846 |
Customer Reviews:
Excellent!.......1997-08-21
One of the best texts on the subject. I also had the opportunity of attending two of Dr. Pao's classes and I highly reccomend it. The book is clearly written, complete and easy to understand if you are into the subject. Warning, not for the math impared
Book Description
This volume looks at financial prediction from a broad range of perspectives. It covers:
- the economic arguments
- the practicalities of the markets
- how predictions are used
- how predictions are made
- how predictions are turned into something usable (asset locations)
It combines a discussion of standard theory with state-of-the-art material on a wide range of information processing techniques as applied to cutting-edge financial problems. All the techniques are demonstrated with real examples using actual market data, and show that it is possible to extract information from very noisy, sparse data sets.
Aimed primarily at researchers in financial prediction, time series analysis and information processing, this book will also be of interest to quantitative fund managers and other professionals involved in financial prediction.
Customer Reviews:
Misleading and Unorganized.......2006-10-14
This is the typical book created putting together technical papers, proceedings, and working papers without a unifying structure.
This is a short list of this book's limitations:
1) Fragmented: every chapter is written by a different author.
2) Unorganized: Neural Networks are introduced only at chapter 11.
3) So badly planned that both chapter 11 and 18 have basically the same content. You can look yourself inside the book to see that.
4) Lack of examples: very few implementations of NN are provided or suggested.
5) Out of context: many chapters are not related to Neural Networks at all, for example chapter 16 is about Yield curve modelling, and chapter 21 is dedicated to Portfolio Optimization without any contextual reference to NN. Please be aware that after introducing these topics there is NO follow-up whatsoever with NN application examples.
6) Misleading: The content about Neural Networks is really minimal.
Average customer rating:
|
Data Mining with Computational Intelligence (Advanced Information and Knowledge Processing)
Lipo Wang , and
Xiuju Fu
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover
Data Mining
| Databases
| Computers & Internet
| Subjects
| Books
General
| Databases
| Computers & Internet
| Subjects
| Books
General
| Algorithms
| Programming
| Computers & Internet
| Subjects
| Books
Genetic
| Algorithms
| Programming
| Computers & Internet
| Subjects
| Books
Expert Systems
| Artificial Intelligence
| Computer Science
| Computers & Internet
| Subjects
| Books
General
| Artificial Intelligence
| Computer Science
| Computers & Internet
| Subjects
| Books
Machine Learning
| Artificial Intelligence
| Computer Science
| Computers & Internet
| Subjects
| Books
Neural Networks
| Artificial Intelligence
| Computer Science
| Computers & Internet
| Subjects
| Books
General
| Computers & Internet
| Subjects
| Books
All Amazon Upgrade
| Amazon Upgrade
| Stores
| Books
Computers & Internet
| Amazon Upgrade
| Stores
| Books
All Titles
| Qualifying Textbooks - Fall 2007
| Stores
| Books
Computers & Internet
| Qualifying Textbooks - Fall 2007
| Stores
| Books
ASIN: 3540245227 |
Book Description
Finding information hidden in data is as theoretically difficult as it is practically important. With the objective of discovering unknown patterns from data, the methodologies of data mining were derived from statistics, machine learning, and artificial intelligence, and are being used successfully in application areas such as bioinformatics, banking, retail, and many others. Wang and Fu present in detail the state of the art on how to utilize fuzzy neural networks, multilayer perceptron neural networks, radial basis function neural networks, genetic algorithms, and support vector machines in such applications. They focus on three main data mining tasks: data dimensionality reduction, classification, and rule extraction. The book is targeted at researchers in both academia and industry, while graduate students and developers of data mining systems will also profit from the detailed algorithmic descriptions.
Customer Reviews:
Revealing the Practical without Being Sidetracked by Gee-Wiz.......2002-09-04
In my college days, I really got into exotic/fun stuff like Fuzzy Logic, Expert Systems, and Case-Based Reasoning. I read lots of books that delved into some author's view of the subtle nuances of some technical refinement or another. I even fancied myself as knowledgeable about Genetic Algorithms and published a research paper on the topic. [Yes, I'm a really exciting guy.] Since then I've come to appreciate that none of these "fancy" methods is perfect and they all have their strengths, weaknesses and best applications. The strength/contribution of "Seven Methods..." is that it rises above the Gee-Wiz details (many of which have come and gone) and instead takes a results oriented approach that summarized where each approach would best succeed in a practical business setting. It has consistently placed in the "top ten" of data-mining reviewers' lists and I think it will continue there for years to come because of this high-level/practical perspective. Each chapter illustrates an alternative data-mining approach using simple/tangible examples. The book ends with seven nontrivial and real world case studies to further illustrate how corporations have come to apply these methods to make (or save) real money.
Do not buy this book.......2002-07-31
This book is not intended for line of business managers that want to really gain insight from data.
It goes into stuff that does not really apply to business needs: brain simulation, neural networks, artificial intelligence. Also, most of the data transformation/usage examples are shallow.
jlpreza
Wien, Osterreich
Bringing AI Down to Earth.......2002-02-27
Excelent Book! The book is well writen in real world language (fortunatelly) and is not for researchers, but for IT and business men. Its content is useful since the first page till the last one. As the authors say, they were asked for the need "to bring the technology down to earth without losing its essence". In fact, bringing IT down to earth (or to business environment) is a difficult task in a world full of one-legged professionals with IT expertise OR business expertise (who knows IT expertise AND business expertise in the same person?) "Seven Methods" shows how to build bridges linking academic to IT and business world. Congratulations for the authors for this brilliant work!
Good Intro to AI and Business Application.......2000-12-29
I read this book after I read Data Mining Techniques For Marketing, Sales and Customer Support. There is a significant overlap between the books, but i suppose each one has a slightly different focus.
While Data Mining Techniques focused and covered techniques most relevant for marketing purposes, Seven Methods is more general with enterprise business intelligence in mind. While rule-based systems and fuzzy logic are absent from Data Mining Techniques, Seven Methods is missing market basket and link analysis.
Although Seven Methods is also written for laypersons, you can still sense that authors are of technical background and have interesting stories to tell about details of each algorithm. On the other hand, authors of Data Mining Techiques are of consulting background or practioner of techniques rather than researcher.
I would suggest that, if you are not a core marketer, Seven Methods will better suit your appetite for learning a range of data mining techniques. If you are a marketer, then read Data Mining Techniques.
Two Books, Identical Content.......2000-08-15
These authors have also written a book titled "Intelligent Decision Support Methods". The two books ("Seven Methods" and "Intelligent Decision Support") are virtually identical. They have the same content, the same graphics, the same layout, the same case studies. The basic content (first 202 of 250 pages) is word for word, page for page IDENTICAL. The ONLY diffference between the 2 books is that "7 Methods" includes suggested solutions for the case studies (adding about 1 page for each of the 7 case studies). Do NOT buy "Intelligent Decision Support Methods". It is twice as expensive (hard cover vs soft cover) with no benefit.
Books:
- What Makes Flamingos Pink?: A Colorful Collection of Q & A's for the Unquenchably Curious
- Why Birds Sing: A Journey Through the Mystery of Bird Song
- Why We Believe What We Believe: Uncovering Our Biological Need for Meaning, Spirituality, and Truth
- A Feast for Crows (A Song of Ice and Fire)
- A Field Guide to the Birds of Borneo, Sumatra, Java, and Bali: The Greater Sunda Islands
- A Field Guide to the Birds of Borneo, Sumatra, Java, and Bali: The Greater Sunda Islands
- A Field Guide to the Birds of Eastern and Central North America
- A Field Guide to the Birds of Eastern and Central North America
- A Field Guide to the Birds of Eastern and Central North America
- A Field Guide to the Birds of Texas and Adjacent States (Peterson Field Guide Series)
Books Index
Books Home
Recommended Books
- Watchdogs of Democracy
- The Minor Prophets: An Exegetical and Expository Commentary : Obadiah, Jonah, Micah, Nahum, and Haba
- The Collected Writings of Zelda Fitzgerald
- The Lives of Dwarfs: Their Journey from Public Curiosity Toward Social Liberation
- The Italian Letter: How the Bush Administration Used a Fake Letter to Build the Case for War in Iraq
- The Feynman Lectures on Physics Volumes 1-2
- The Site Book : A Field Guide to Commercial Real Estate Evaluation
- Hablando claro
- Taxation, information exchange : agreement between the United States of America and Guyana, signed a
- Closing Down the American Base at Adak, Alaska: The Social and Psychological Trauma of Relocating Mi
Spikes: Exploring the Neural Code (Computational Neuroscience)
Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience)
Dynamical Systems in Neuroscience: The Geometry of Excitability and Brusting (Computational Neuroscience)
Spiking Neuron Models
Principles of Neural Science
