Reviews (4)

Psychology & Neurophysiology of Vision Science
Stephen Palmer has written a marvelous book. Its well organized and written.It is suitable as a reference & text for those beginning and advancing to higher levels in Vision Science. The pictures, diagrams, graphs, charts, photos, and outlines are well placed and explained in the body of the text. Because Dr. Palmer is first a Neuropsychologist, the approach is more geared toward psychological mechanisms & psychophysics. The emphasis is less on Biological Approaches to Visual Function. This & Chalupa's 2 Volume Set should get novice to intermediate scientists going further in Vision Science!

I can't believe it's an one-author book
This book covers neuro, behavioral, computer science, almost everything about vision science, and very organized. at the bottom line, this book can be a good reference for vision science.

Editorial Review

Reviews (4)

PhD Student
This book is really just an introduction to NLA and intended for the novice although it assumes some LA background.It is not necessary to read this if you plan PhD studies in the field since the book is only an introduction to the material and is honestly too simple.Read anything by Bjorck or Van Loan if you are a serious PhD student and want to do research in the field of NLA.This book is written for students that really don't plan research in the field of NLA but rather may need some skill level in NLA to perform their job or their own own research which may have some NLA exposure.

This book grows on you
I used this text for a two-semester graduate sequence in numerical linear algebra (NLA) while I was a graduate student in the Mathematics Department at The University of Kentucky.If you do not have a substantial background in linear algebra and numerical analysis, which I did not when I first used this book, the material covered and the presentation can seem to be quite daunting.But while the presentation is very thorough, it is not unnecessarily so.After I had used this text for about three months, I grew accustomed to the very detailed nature of the writing and grateful for the sheer level of information contained in a meer 419 pages.

Many introductury numerical analysis books include several chapters covering the commonly used algorithms in NLA but usually not in complete detail.While this format is friendlier to use for an overview of the "basics," in the real world, the standard ways of solving numerical systems such as GEPP, SVD, QR, Cholesky decompostions, Gauss-Siedel iterations, and other methods do not always work in a nice cookbook-like fashion.When one of these standard methods that engineers and research scientists use to solve "standard" problems fails, and it will sometimes, this book will give you a good starting point to figure out what went wrong and what alternate methods can be used to solve a linear system that is not as easy as it first appeared to be.

If you are learning NLA, you are probably doing so because you either want to or have to apply it in your professional life, by which I mean your job or the job that you hope to get.In my current position, I develop and design statistical and deterministic simulators for human genetics research.And when I need to used Cholesky decompostions, SVD's, and other NLA methods, I always consult this book to review how these methods work and, more importantly, what innocent looking data will cause these methods to fail silently - in other words, give results that look reasonable, but are completely wrong.In conclusion, this book is not the easiest to read.But it is one of the best resources available when you need to learn how to handle basic and not-so-basic problems in the field of NLA.

Reviews (1)

Reviews (13)

A big gap in model averaging
Model averaging is an integral part of model selection and prediction.
Bayesian model averaging (BMA) is a highly sophisticated method for doing model averaging.
It is amazing then that the book hardly touches upon BMA or Bayesian methods.
(By my counting there is a total of 7 pages in all).

In my opinion the authors are very weak in this area which
explains why the topic of BMA is not covered.This is a
shame because it is more than likely BMA would be a serious
competitor (if not better) than the other methods they are
familiar with and discuss in the text.

The Elements of Statistical Learning
The book is written by some of the biggest names currently in the field, and thus is written at a certain level, this isn't a fault of the book or the authers, but rather it was written for a specific audience.However I did find it odd when they would occassionally explain basic readily known notation, but later on assume the reader is familiar with what I would regard as advanced notation, or leave out quite a few steps in their mathematics assuming the reader understands what they did.This book covers a wide range of techniques ranging from the more traditional to the current, and for each topic presents an overview of the technique and provides adequate references for further exploration.

The reader should have a good underlying understanding of linear algebra, statistics and probability theory and also be familiar with the techniques presented here.This book was used in a graduate engineering data mining class, and most of us struggled greatly with the book.This book probably would have been more appropriate if this was a book to augment another text, or if this had not been the first time we had seen topics such as those presented, this being the book to explain neural networks, support vector machines and whatnot when you've never seen them before makes for a very bewildering experience, but once you find a few journal articles the techniques actually are fairly easy to understand.

The book does not explain how to implement using software any of the techniques, this is a topic left up to other books, such as Modern Applied Statistics with S by Ripley and Venerables, and only in their discussion about apriori for association rules did I see that they state a software package.It would have been nice if they would have given some insight into how they created some of the great graphics that punctuate the book, perhaps as additional material on the website.

A book that is more down to earth for engineers, albeit different in scope, would be Duda and Hart's Pattern Classification, which I believe are electrical engineers and written more from an engineering standpoint.In addition the Duda and Hard book gives a lot of applications-based problems and has an associated MATLAB handbook to walk readers through building many types of learners, while this book the end-of-chapter excercises are almost exclusively proofs and theoretical excercises.Not a fault of the book, but rather just a difference and depends on what the reader wants to get out of it.

Ultimately, even though it did prove to be a rather confusing book, I have learned a lot from it and will continue to go through it to learn even more from it as it does tend to become more lucid the more I go through it.

Reviews (2)

Recommended, but not the place to begin
The title of the book is somewhat misleading, in that most of the research papers involve advanced issues concerning one particular graphical model, namely the Bayesian network. For this reason I highly recommend, as a prerequisite to this book, Finn Jensen's "Bayesian Networks and Decision Graphs". Jensen's book is adequate in giving a good introduction and overview of the subject, but not sufficient for calling oneself an "expert" upon successfully digesting it.

To its credit, "Learning in Graphical Models" has several well-written and interesting papers, but the tutorial papers just did not seem enough of an introduction for me to feel comfortable using it as a first source of introduction.

What I find most compelling about Bayesian networks is the fact that they seem both highly modular (which facilitates reusability and network interconnectivity) and can be designed in a semi-rational manner (contrast this with neural-network architectures for which few good algorithms exist for determining size and number of layers). For this reason I imagine they will be important players in future engineering projects that require learning and adaptation.