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As machine learning becomes more and more available to the general public, theoretical questions are turning into pressing practical issues.
Skew variation, a rejoinder
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Das Fehlergesetz und Seine Verallgemeiner-Ungen Durch Fechner und Pearson. A Rejoinder
Karl Pearson · 1905
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Karl Pearson · 1920
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Robust tests for equality of variances: In ingram olkin, harold hotelling, et alia, 1960
Howard Levene · 1960
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Dynamic programming
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Linear programming approaches to the convex hull problem in rm
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A note on the equality of the column, and row rank of a matrix
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Leo Breiman · 2001
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Rademacher and Gaussian complexities: Risk bounds and structural results
Peter L Bartlett and Shahar Mendelson · 2002
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General conditions for predictivity in learning theory
Tomaso Poggio, Ryan Rifkin, Sayan Mukherjee, and Partha Niyogi · 2004
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Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time
Daniel A Spielman and Shang-Hua Teng · 2004
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Convex optimization
Stephen Boyd and Lieven Vandenberghe · 2004
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Pareto-front exploitation in symbolic regression
Guido F Smits and Mark Kotanchek · 2005
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Evaluation of classifiers for an uneven class distribution problem
Sophia Daskalaki, Ioannis Kopanas, and Nikolaos Avouris · 2006
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On the hardness of computing intersection, union and minkowski sum of polytopes
Hans Raj Tiwary · 2008
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Mutual information between discrete and continuous data sets
Brian C Ross · 2014
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Deepdriving: Learning affordance for direct perception in autonomous driving
Chenyi Chen, Ari Seff, Alain Kornhauser, and Jianxiong Xiao · 2015
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Adaptive control processes: a guided tour
Richard E Bellman · 2015
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On the uniform convergence of relative frequencies of events to their probabilities
Vladimir N Vapnik and A Ya Chervonenkis · 2015
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Association, correlation and causation
Naomi Altman and Martin Krzywinski · 2015
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Distilling free-form natural laws from experimental data
Michael Schmidt and Hod Lipson · 2009
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Elementary linear algebra: applications version
Howard Anton and Chris Rorres · 2010
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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Principal component analysis
Ian Jolliffe · 2011
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Dual coordinate descent methods for logistic regression and maximum entropy models
Hsiang-Fu Yu, Fang-Lan Huang, and Chih-Jen Lin · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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The WEKA workbench. online appendix for data mining: Practical machine learning tools and techniques
Frank Eibe, MA Hall, and IH Witten · 2016
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Points of significance: model selection and overfitting, 2016
Jake Lever, Martin Krzywinski, and Naomi Altman · 2016
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Points of significance: Logistic regression, 2016
Jake Lever, Martin Krzywinski, and Naomi Altman · 2016
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
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Points of significance: Ensemble methods: bagging and random forests, 2017
Naomi Altman and Martin Krzywinski · 2017
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Effects of dataset characteristics on the performance of feature selection techniques
Dijana Oreski, Stjepan Oreski, and Bozidar Klicek · 2017
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Points of significance: Classification and regression trees, 2017
Martin Krzywinski and Naomi Altman · 2017
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The earth is flat (p> 0.05): significance thresholds and the crisis of unreplicable research
Valentin Amrhein, Fränzi Korner-Nievergelt, and Tobias Roth · 2017
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Why does deep and cheap learning work so well?
Henry W Lin, Max Tegmark, and David Rolnick · 2017
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The curse (s) of dimensionality
Naomi Altman and Martin Krzywinski · 2018
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Understanding cancer phenomenon at gene-expression level by using a shallow neural network chain
Pietro Barbiero, Andrea Bertotti, Gabriele Ciravegna, Giansalvo Cirrincione, Elio Piccolo, and Alberto Tonda · 2018
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Automated machine learning for predictive modeling
Datarobot · 2019
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