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initial port of documentation to md, algorithms reference converted to md, template based on spark docs
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‎docs/README.md‎

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# SystemML Documentation
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The primary SystemML documentation is written in markdown (*.md) and can be converted to HTML using
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[Jekyll](http://jekyllrb.com).
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Jekyll (and optionally Pygments) can be installed on the Mac OS in the following manner.
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$ brew install ruby
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$ gem install jekyll
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$ gem install jekyll-redirect-from
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$ brew install python
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$ pip install Pygments
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For installation on other platforms, please consult the Jekyll documentation.
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To generate SystemML documentation in HTML, navigate to the ```docs``` folder, the root directory of the
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documentation. From there, you can have Jekyll convert the markdown files to HTML. If you run in server mode,
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Jekyll will serve up the generated documentation by default at http://127.0.0.1:4000. Modifications
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to *.md files will be converted to HTML and can be viewed in a web browser.
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$ jekyll serve -w

‎docs/_config.yml‎

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highlighter: pygments
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markdown: kramdown
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gems:
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- jekyll-redirect-from
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# For some reason kramdown seems to behave differently on different
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# OS/packages wrt encoding. So we hard code this config.
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kramdown:
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entity_output: numeric
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include:
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- _static
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- _modules
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# These allow the documentation to be updated with newer releases
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SYSTEMML_VERSION: 5.2

‎docs/_layouts/global.html‎

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<!DOCTYPE html>
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<!--[if lt IE 7]> <html class="no-js lt-ie9 lt-ie8 lt-ie7"> <![endif]-->
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<!--[if IE 7]> <html class="no-js lt-ie9 lt-ie8"> <![endif]-->
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<!--[if IE 8]> <html class="no-js lt-ie9"> <![endif]-->
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<!--[if gt IE 8]><!--> <html class="no-js"> <!--<![endif]-->
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<head>
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<title>{{ page.title }} - SystemML {{site.SYSTEMML_VERSION}}</title>
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<meta charset="utf-8">
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<meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1">
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{% if page.description %}
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<meta name="description" content="{{page.description | replace: 'SYSTEMML_VERSION', site.SYSTEMML_VERSION}}">
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{% endif %}
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<meta name="viewport" content="width=device-width">
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<link rel="stylesheet" href="css/bootstrap.min.css">
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<link rel="stylesheet" href="css/bootstrap-responsive.min.css">
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<link rel="stylesheet" href="css/main.css">
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<link rel="stylesheet" href="css/pygments-default.css">
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<script src="js/vendor/modernizr-2.6.1-respond-1.1.0.min.js"></script>
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</head>
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<body>
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<!--[if lt IE 7]>
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<p class="chromeframe">You are using an outdated browser. <a href="http://browsehappy.com/">Upgrade your browser today</a> or <a href="http://www.google.com/chromeframe/?redirect=true">install Google Chrome Frame</a> to better experience this site.</p>
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<![endif]-->
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<div class="navbar navbar-fixed-top" id="topbar">
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<div class="navbar-inner">
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<div class="container">
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<div class="brand" style="padding: 15px 0px; font-size: 20px; font-style: italic; font-weight: bold;"><a href="index.html">SystemML - {{site.SYSTEMML_VERSION}}</a>
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</div>
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<ul class="nav">
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<li><a href="index.html">Home</a></li>
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<li class="dropdown">
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<a href="#" class="dropdown-toggle" data-toggle="dropdown">Documentation<b class="caret"></b></a>
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<ul class="dropdown-menu">
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<li><a href="http://www.github.com/SparkTC/systemml">SystemML GitHub README</a></li>
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<li><a href="algorithms-reference.html">Algorithms Reference</a></li>
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<!-- <li><a href="dml-language-reference.html">DML Language Reference</a></li> -->
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<li class="divider"></li>
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<li>&nbsp;&nbsp;&nbsp;&nbsp; More Coming Soon...</li>
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</ul>
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</li>
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</ul>
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</div>
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</div>
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</div>
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<div class="container" id="content">
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{% if page.displayTitle %}
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<h1 class="title">{{ page.displayTitle }}</h1>
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{% else %}
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<h1 class="title">{{ page.title }}</h1>
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{% endif %}
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{{ content }}
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</div> <!-- /container -->
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<script src="js/vendor/jquery-1.8.0.min.js"></script>
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<script src="js/vendor/bootstrap.min.js"></script>
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<script src="js/vendor/anchor.min.js"></script>
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<script src="js/main.js"></script>
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<!-- MathJax Section -->
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<script type="text/x-mathjax-config">
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MathJax.Hub.Config({
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TeX: { equationNumbers: { autoNumber: "AMS" } }
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});
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</script>
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<script>
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// Note that we load MathJax this way to work with local file (file://), HTTP and HTTPS.
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// We could use "//cdn.mathjax...", but that won't support "file://".
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(function(d, script) {
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script = d.createElement('script');
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script.type = 'text/javascript';
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script.async = true;
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script.onload = function(){
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MathJax.Hub.Config({
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tex2jax: {
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inlineMath: [ ["$", "$"], ["\\\\(","\\\\)"] ],
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displayMath: [ ["$$","$$"], ["\\[", "\\]"] ],
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processEscapes: true,
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skipTags: ['script', 'noscript', 'style', 'textarea', 'pre']
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}
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});
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};
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script.src = ('https:' == document.location.protocol ? 'https://' : 'http://') +
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'cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML';
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d.getElementsByTagName('head')[0].appendChild(script);
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}(document));
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</script>
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</body>
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</html>

‎docs/algorithms-bibliography.md‎

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---
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layout: global
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title: SystemML Algorithms Reference - Bibliography
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displayTitle: <a href="algorithms-reference.html">SystemML Algorithms Reference</a>
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---
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AcockStavig1979, Alan C. Acock and Gordon
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R. Stavig, A Measure of Association for Nonparametric
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Statistics, Social Forces, Oxford University
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Press, Volume 57, Number 4, June, 1979,
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1381--1386.
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AgrawalKSX2002, Rakesh Agrawal and
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Jerry Kiernan and Ramakrishnan Srikant and Yirong Xu,
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Hippocratic Databases, Proceedings of the 28-th
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International Conference on Very Large Data Bases (VLDB 2002),
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Hong Kong, China, August 20--23, 2002,
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143--154.
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Agresti2002, Alan Agresti, Categorical
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Data Analysis, Second Edition, Wiley Series in
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Probability and Statistics, Wiley-Interscience
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2002, 710.
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AloiseDHP2009, Daniel Aloise and Amit
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Deshpande and Pierre Hansen and Preyas Popat, NP-hardness of
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Euclidean Sum-of-squares Clustering, Machine Learning,
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Kluwer Academic Publishers, Volume 75, Number 2,
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May, 2009, 245--248.
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ArthurVassilvitskii2007,
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k-means++: The Advantages of Careful Seeding, David
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Arthur and Sergei Vassilvitskii, Proceedings of the 18th
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Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2007),
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January 7--9, 2007, New Orleans, LA,
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USA, 1027--1035.
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Chapelle2007, Olivier Chapelle, Training a Support Vector Machine in the Primal, Neural Computation, 2007.
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Cochran1954, William G. Cochran,
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Some Methods for Strengthening the Common $\chi^2$ Tests,
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Biometrics, Volume 10, Number 4, December
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1954, 417--451.
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Gill2000, Jeff Gill, Generalized Linear
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Models: A Unified Approach, Sage University Papers Series on
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Quantitative Applications in the Social Sciences, Number 07-134,
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2000, Sage Publications, 101.
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Hartigan1975, John A. Hartigan,
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Clustering Algorithms, John Wiley~&~Sons Inc.,
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Probability and Mathematical Statistics, April
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1975, 365.
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Hsieh2008, C-J Hsieh, K-W Chang, C-J Lin, S. S. Keerthi and S. Sundararajan, A Dual Coordinate Descent Method for Large-scale Linear SVM, International Conference of Machine Learning (ICML), 2008.
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Lin2008, Chih-Jen Lin and Ruby C. Weng and
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S. Sathiya Keerthi, Trust Region Newton Method for
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Large-Scale Logistic Regression, Journal of Machine Learning
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Research, April, 2008, Volume 9, 627--650.
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McCallum1998, A. McCallum and K. Nigam, A comparison of event models for naive bayes text classification, AAAI-98 workshop on learning for text categorization, 1998.
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McCullagh1989, Peter McCullagh and John Ashworth
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Nelder, Generalized Linear Models, Second Edition,
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Monographs on Statistics and Applied Probability, Number 37,
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1989, Chapman & Hall/CRC, 532.
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Nelder1972, John Ashworth Nelder and Robert
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William Maclagan Wedderburn, Generalized Linear Models,
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Journal of the Royal Statistical Society, Series A
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(General), 1972, Volume 135, Number 3,
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370--384.
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Nocedal1999, J. Nocedal and S. J. Wright, Numerical Optimization, Springer-Verlag, 1999.
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Nocedal2006, Optimization Numerical Optimization,
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Jorge Nocedal and Stephen Wright, Springer Series
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in Operations Research and Financial Engineering, 664,
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Second Edition, Springer, 2006.
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Russell2009, S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, 2009.
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Scholkopf1995, B. Scholkopf, C. Burges and V. Vapnik, Extracting Support Data for a Given Task, International Conference on Knowledge Discovery and Data Mining (ICDM), 1995.
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Stevens1946, Stanley Smith Stevens,
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On the Theory of Scales of Measurement, Science
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June 7, 1946, Volume 103, Number 2684,
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677--680.
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Vetterling1992,
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W. T. Vetterling and B. P. Flannery,
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Multidimensions in Numerical Recipes in C - The Art in Scientific Computing, W. H. Press and S. A. Teukolsky (eds.), Cambridge University Press, 1992.

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