Author: reinwald
Date: Fri Mar 30 04:31:05 2018
New Revision: 1828046
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systemml/site/docs/1.1.0/algorithms-descriptive-statistics.html
systemml/site/docs/1.1.0/algorithms-matrix-factorization.html
systemml/site/docs/1.1.0/algorithms-reference.html
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systemml/site/docs/1.1.0/reference-guide-caffe2dml.html
systemml/site/docs/1.1.0/release-creation-process.html
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systemml/site/docs/1.1.0/troubleshooting-guide.html
Added: systemml/site/docs/1.1.0/algorithms-bibliography.html
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Algorithms Reference</a></h1>
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+<h1 id="bibliography">7. Bibliography</h1>
+
+<p><strong>[AcockStavig1979]</strong> Alan C. Acock and Gordon
+R. Stavig, A Measure of Association for Nonparametric
+Statistics, Social Forces, Oxford University
+Press, Volume 57, Number 4, June, 1979,
+1381–1386.</p>
+
+<p><strong>[AgrawalKSX2002]</strong> Rakesh Agrawal and
+Jerry Kiernan and Ramakrishnan Srikant and Yirong Xu,
+Hippocratic Databases, Proceedings of the 28-th
+International Conference on Very Large Data Bases (VLDB 2002),
+Hong Kong, China, August 20–23, 2002,
+143–154.</p>
+
+<p><strong>[Agresti2002]</strong> Alan Agresti, Categorical
+Data Analysis, Second Edition, Wiley Series in
+Probability and Statistics, Wiley-Interscience
+2002, 710.</p>
+
+<p><strong>[AloiseDHP2009]</strong> Daniel Aloise and Amit
+Deshpande and Pierre Hansen and Preyas Popat, NP-hardness of
+Euclidean Sum-of-squares Clustering, Machine Learning,
+Kluwer Academic Publishers, Volume 75, Number 2,
+May, 2009, 245–248.</p>
+
+<p><strong>[ArthurVassilvitskii2007]</strong>
+k-means++: The Advantages of Careful Seeding, David
+Arthur and Sergei Vassilvitskii, Proceedings of the 18th
+Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2007),
+January 7–9, 2007, New Orleans, LA,
+USA, 1027–1035.</p>
+
+<p><strong>[Breiman2001]</strong> L. Breiman. Random forests. Machine
Learning, 45(1):5â32, 2001.</p>
+
+<p><strong>[BreimanFOS1984]</strong> L. Breiman, J. H. Friedman, R. A. Olshen,
and C. J. Stone. Classification and Regression Trees. Wadsworth, 1984.</p>
+
+<p><strong>[Chapelle2007]</strong> Olivier Chapelle, Training a Support Vector
Machine in the Primal, Neural Computation, 2007.</p>
+
+<p><strong>[Cochran1954]</strong> William G. Cochran,
+Some Methods for Strengthening the Common $\chi^2$ Tests,
+Biometrics, Volume 10, Number 4, December
+1954, 417–451.</p>
+
+<p><strong>[Collett2003]</strong> D. Collett. Modelling Survival Data in
Medical Research, Second Edition. Chapman & Hall/CRC Texts in Statistical
Science. Taylor & Francis, 2003.</p>
+
+<p><strong>[Gill2000]</strong> Jeff Gill, Generalized Linear
+Models: A Unified Approach, Sage University Papers Series on
+Quantitative Applications in the Social Sciences, Number 07-134,
+2000, Sage Publications, 101.</p>
+
+<p><strong>[Hartigan1975]</strong> John A. Hartigan,
+Clustering Algorithms, John Wiley~&~Sons Inc.,
+Probability and Mathematical Statistics, April
+1975, 365.</p>
+
+<p><strong>[Hsieh2008]</strong> 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.</p>
+
+<p><strong>[Lin2008]</strong> Chih-Jen Lin and Ruby C. Weng and
+S. Sathiya Keerthi, Trust Region Newton Method for
+Large-Scale Logistic Regression, Journal of Machine Learning
+Research, April, 2008, Volume 9, 627–650.</p>
+
+<p><strong>[McCallum1998]</strong> A. McCallum and K. Nigam, A comparison of
event models for naive bayes text classification, AAAI-98 workshop on learning
for text categorization, 1998.</p>
+
+<p><strong>[McCullagh1989]</strong> Peter McCullagh and John Ashworth
+Nelder, Generalized Linear Models, Second Edition,
+Monographs on Statistics and Applied Probability, Number 37,
+1989, Chapman & Hall/CRC, 532.</p>
+
+<p><strong>[Nelder1972]</strong> John Ashworth Nelder and Robert
+William Maclagan Wedderburn, Generalized Linear Models,
+Journal of the Royal Statistical Society, Series A
+(General), 1972, Volume 135, Number 3,
+370–384.</p>
+
+<p><strong>[Nocedal1999]</strong> J. Nocedal and S. J. Wright, Numerical
Optimization, Springer-Verlag, 1999.</p>
+
+<p><strong>[Nocedal2006]</strong> Optimization Numerical Optimization,
+Jorge Nocedal and Stephen Wright, Springer Series
+in Operations Research and Financial Engineering, 664,
+Second Edition, Springer, 2006.</p>
+
+<p><strong>[PandaHBB2009]</strong> B. Panda, J. Herbach, S. Basu, and R. J.
Bayardo. PLANET: massively parallel learning of tree ensembles with mapreduce.
PVLDB, 2(2):1426â 1437, 2009.</p>
+
+<p><strong>[Russell2009]</strong> S. Russell and P. Norvig, Artificial
Intelligence: A Modern Approach, Prentice Hall, 2009.</p>
+
+<p><strong>[Scholkopf1995]</strong> B. Scholkopf, C. Burges and V. Vapnik,
Extracting Support Data for a Given Task, International Conference on Knowledge
Discovery and Data Mining (ICDM), 1995.</p>
+
+<p><strong>[Stevens1946]</strong> Stanley Smith Stevens,
+On the Theory of Scales of Measurement, Science
+June 7, 1946, Volume 103, Number 2684,
+677–680.</p>
+
+<p><strong>[Vetterling1992]</strong>
+W. T. Vetterling and B. P. Flannery,
+Multidimensions in Numerical Recipes in C - The Art in Scientific Computing,
W. H. Press and S. A. Teukolsky (eds.), Cambridge University Press, 1992.</p>
+
+<p><strong>[ZhouWSP08]</strong>
+Y. Zhou, D. M. Wilkinson, R. Schreiber, and R. Pan. Large-scale parallel
collaborative filtering for the Netflix prize.
+In Algorithmic Aspects in Information and Management, 4th International
Conference, AAIM 2008, Shanghai, China, June 23-25, 2008. Proceedings, pages
337â348, 2008.</p>
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