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new task: machine-learning?

Dear Colleagues,

Despite my hesitation with what name to choose (I would like it to be
"Statistical Learning", but that would have excluded some other
"learning" strategies I guess), I've decided to initiate "Machine
Learning" task within science blend.

I wanted to check with you first, either it looks reasonable/desired
before I commit it.  Please find a tentative task file attached.

=------------------------------   /v\  ----------------------------=
Keep in touch                    // \\     (yoh@|www.)onerussian.com
Yaroslav Halchenko              /(   )\               ICQ#: 60653192
                   Linux User    ^^-^^    [175555]

Task: Machine Learning
Description: Debian Science Machine Learning packages
 This metapackage will install Debian packages which might be useful for
 scientists interested in machine learning.  Included packages range
 from knowledge-based (expert) inference systems to software
 implementing dominant nowadays statistical methods.

Depends: python-pyke, gprolog, yap
Comments: Prolog (and alike) systems for inductive reasoning

Depends: libtorch3-dev

Depends: libshogun-dev, libfann-dev, libsvm-dev, libcomplearn-dev
Comment: above libraries have also variety of interfaces to high-level
 scripting languages (e.g. Python) and even possibly some interactive GUI

Depends: python-scikits-learn, python-mdp, python-mlpy
Comment: Native Python toolkits

Depends: weka

Depends: vowpal-wabbit

Depends: r-cran-mass, r-cran-bayesm, r-cran-class, r-cran-cluster,
Comment: R packages

Depends: python-mvpa
Why: Framework for statistical learning analysis of large datasets.
Published-Title: PyMVPA: a unifying approach to the analysis of neuroscientific data
Published-Authors: Michael Hanke, Yaroslav O. Halchenko, Per B. Sederberg, Emanuele Olivetti, Ingo Fründ, Jochem W. Rieger, Christoph S. Herrmann, James V. Haxby, Stephen José Hanson, Stefan Pollmann
Published-In: Frontiers in Neuroinformatics, 3:3
Published-Year: 2009
Published-URL: http://www.frontiersin.org/neuroinformatics/paper/10.3389/neuro.11/003.2009/
Published-DOI: 10.3389/neuro.11.003.2009

Depends: python-scikits-statsmodels
Homepage: http://statsmodels.sourceforge.net/
Language: Python
License: BSD
Responsible: Yaroslav Halchenko <debian@onerussian.com>
Pkg-URL: http://neuro.debian.net/pkgs/python-scikits-statsmodels.html
WNPP: 570604
Why: Statistical models
Description: classes and functions for the estimation of statistical models
 scikits.statsmodels is a pure Python package that provides classes
 and functions for the estimation of several categories of statistical
 models. These currently include linear regression models, OLS, GLS,
 WLS and GLS with AR(p) errors, generalized linear models for six
 distribution families and M-estimators for robust linear models. An
 extensive list of result statistics are available for each estimation

Suggests: science-statistics

Suggests: science-typesetting
Meta-Suggests: svn://svn.debian.org/blends/projects/science/trunk/debian-science/tasks/typesetting

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