You might also want to consult:

Duda RO, Hart PE, Stork DG (2001) Pattern Classification (John Wiley &
Sons, New York, ed 2).
Its known to be an important resource for the field, covering statistical
learning and various classification techniques.

Cheers

Fraser.


On 2:15 am 01/27/10 Carlton CHU <[log in to unmask]> wrote:
>
> The second edition of &quot;The Elements of Statistical
Learning&quot; is actually free to download online. http://www-stat.stan
> ford.edu/~tibs/ElemStatLearn/
> Personally, I like the book by Christopher Bishop, &quot;Pattern
> Recognition and Machine Learning&quot;. http://research.microsoft.com/

> en-us/um/people/cmbishop/prml/
> This book takes more Bayesian view of statistical learning, which
> will also help you understand a lot of publications related to SPM, as

> the method&#39;s group@FIL is pretty Bayesian oriented. 

>
>
> If you want some comprehensive readings related to pattern
> recognition for fMRI or anatomical MRI, you can have a look of my

> thesis, titled &quot;Pattern recognition and machine learning for
> magnetic resonance images with kernel methods&quot;

> http://eprints.ucl.ac.uk/18519/ My thesis describes required
> pre-processing and steps to utilize standard kernel methods, such as
> SVM, for fMRI decoding or classification of neurodegenerative
> diseases. If you want some of the matlab codes, you can also email
> me.
>

> By the way, I was John Ashburner and Karl Friston&#39;s PhD Student,

> so my work is tightly related to SPM.
>
>
> Carlton
>

> On Wed, Jan 20, 2010 at 2:36 AM, Roberto Viviani  wrote:
> I did some work some years ago on this topic, and I can recommend a
> couple of books. One can go much further with some general solid
> knowledge of machine learning in itself.
>
> The book by Hastie, Tibshirani, and Friedman, The Elements of
> Statistical Learning, now at its 2nd edition, changed my thinking
> completely. This book treats machine learning algorithms from the
> perspective of applied statistics (mostly through an aggressive use of
> regression methodology), showing how applied statistical thinking can
> inform this apparently different area. Concise, crisp, and
> intellectually satisfying.
>
> Probably the most successful product of machine learning is the
> support vector machine. A very readable and useful compact book is An
> Introduction to Support Vector Machines by Cristianini and
> Shawe-Taylor, with an update in 2004. The fascinating story about SVM
> is that it developed behind the iron curtain in isolation through the
> interbreeding of the Russian probabilist school and artificial
> intelligence. One can read about this in the book by its creator,
> Vladimir Vapnik, The Nature of Statistical Learning Theory. After the

> wall came down, Vapnik moved to AT&amp;T, then to NEC.

>
> Best,
> Roberto Viviani
> University of Ulm, Germany
>
>
>

> I don&#39;t know of any courses, but the Video Lectures web site has

> some nice stuff.  Maybe have a look through:
>
> http://videolectures.net/fmri06_whistler/
> http://videolectures.net/Top/Computer_Science/Machine_Learning/
>
> Best regards,
> -John
>
> On Mon, 2010-01-18 at 15:53 +0100, Wouter De Baene wrote:
> Dear all,
>
> Although my question is not directly SPM-related, it is related to
> this previous mail. Does anybody know of short courses on pattern
> recognition related to fMRI?
>
> Best regards,
> Wouter De Baene
>
>

> On 18/01/10 15:40, &quot;Jonas Richiardi&quot;  wrote:
>
> >  Dear Colleagues, please accept our apologies for multiple postings.
> >
> >  * CALL FOR PAPERS *
> >
> >  1st ICPR Workshop on Brain Decoding:
> >   Pattern Recognition Challenges in Neuroimaging
> >
> >  In conjunction with the
> >  20th International Conference on Pattern Recognition
> >  August 22, 2010, Istanbul, Turkey
> >
> >  http://miplab.epfl.ch/icpr2010/
> >
> >  Many of the challenges facing brain decoding are also highly
> >  relevant to the field of pattern recognition at large -
> >  classification of multivariate time-series, dimensionality
> > reduction, and causal modelling to name a few.
> >  This workshop wants to bring together researchers in pattern
> >  recognition and neuroimaging for fruitful exchanges of experiences
> >  and recent developments in brain decoding, both on the
> >  methodological side and the application side. The temporal and
> >  spatial proximity with the leading pattern recognition conference
> >  ICPR is a great opportunity to learn about newest scientific
> >  developments in order to expand brain decoding methodology.
> >
> >  SCOPE
> >
> >  The topics of interest include:
> >
> >  * Data representations for brain decoding (MRI/fMRI/EEG/...)
> >  - Voxel and feature selection
> >  - Linear and non-linear dimensionality reduction
> >  - Sparse timecourse representations
> >
> >  * Classifiers for high-dimensional learning
> >  - Regularisation schemes
> >  - Subspace methods
> >  - Interpretability and validation
> >
> >  * Applications of brain decoding
> >  - Visual processing
> >  - Man-machine interfaces
> >  - Clinical applications
> >  - Deception detection
> >
> >  SUBMISSION AND PROCEEDINGS
> >
> >  Authors should prepare full 4-pages papers (double-column, IEEE
> >  style). Manuscripts will be evaluated by 2 reviewers.
> >
> >  Proceedings will be published by IEEE Computer Science Society in
> >  electronic format. They will be permantently available on the
> >  IEEExplore and IEEE CS Digital Library  online repositories, and
> >  indexed in IEE INSPEC, EI Compendex (Elsevier), and others.
> >
> >  DATES AND DEADLINES
> >
> >  - April 1, 2010: Paper submission deadline
> >  - May 1, 2010: Acceptance Notification
> >  - May 14, 2010: Early registration deadline
> >  - June 1, 2010: Camera-ready paper due
> >  - August 22, 2010: Workshop
> >
> >  PROGRAMME COMMITTEE
> >
> >  Rafeef Abugharbieh, University of British Columbia (Canada)
> >  Tulay Adali, University of Maryland, Baltimore County (USA)
> >  John Ashburner, University College London (UK)
> >  Patricia Besson, CNRS (France)
> >  Mick Brammer, King¹s College London (UK)
> >  Vince Calhoun, Yale University (USA)
> >  Thomas Ethofer, University of Tübingen (Germany)
> >  Christian Gaser, University of Jena (Germany)
> >  Ghassan Hamarneh, Simon Fraser University (Canada)
> >  David Hardoon, Institute for Infocomm Research (Singapore)
> >  Krzysztof Kryszczuk, IBM Research Zürich (Switzerland)
> >  Raghu Machiraju, Ohio State University (USA)
> >  Andre Marquand, King¹s College London (UK)
> >  Julien Meynet, Yahoo! research (France)
> >  Torsten Möller, Simon Fraser University (Canada)
> >  Joao Sato, Federal University of ABC (Brazil)
> >  Sophie Schwartz, University of Geneva (Switzerland)
> >  Stephen Strother, University of Toronto (Canada)
> >  Bertrand Thirion, NeuroSpin Paris (France)
> >  Marc Van Hulle, K.U. Leuven (Belgium)
> >  Patrik Vuilleumier, University of Geneva (Switzerland)
> >
> >  ORGANISING COMMITTEE
> >
> >  Jonas Richiardi, Ecole Polytechnique Fédérale de Lausanne (EPFL)
> >  and University of Geneva (Swizerland)
> >  Dimitri Van De Ville, Ecole Polytechnique Fédérale de Lausanne
> >  (EPFL) and University of Geneva (Switzerland)
> >  Christos Davatzikos, University of Pennsylvania (USA)
> >  Janaina Mourão-Miranda, University College London

> >  and King&#39;s College London (UK)

> >
> >  details: http://miplab.epfl.ch/icpr2010/
> >  contact: wbd2010 _AT_ listes.epfl.ch
>
>
>
> --
> John Ashburner