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Statistical learning theory
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#	Item
11	Lecture Notes on Online Learning DRAFT Alexander Rakhlin —– These lecture notes contain material presented in the Statistical Learning Theory course at UC Berkeley, Spring’08. Add to Reading List Source URL: www-stat.wharton.upenn.edu Language: English - Date: 2009-01-13 18:04:13
12	Stat 928: Statistical Learning Theory Lecture: 11 Symmetrization and Rademacher Averages Instructor: Sham Kakade Add to Reading List Source URL: stat.wharton.upenn.edu Language: English - Date: 2011-02-22 01:44:09
13	Support Vector Machine (and Statistical Learning Theory) Tutorial Jason Weston NEC Labs America Add to Reading List Source URL: www.cs.columbia.edu - Date: 2006-11-09 16:05:24
14	Introduction to Statistical Learning Theory Olivier Bousquet1 , St´ephane Boucheron2 , and G´abor Lugosi3 1 Max-Planck Institute for Biological Cybernetics Spemannstr. 38, DT¨ Add to Reading List Source URL: 84.89.132.1 - Date: 2004-07-08 09:05:04
15	Computational Learning Theory for Artificial Neural Networks Martin Anthony and Norman Biggs Department of Statistical and Mathematical Sciences, London School of Economics and Political Science, Houghton St., London WC2 Add to Reading List Source URL: www.maths.lse.ac.uk - Date: 2000-04-03 14:22:19
16	Spectral learning of linear dynamics from generalised-linear observations with application to neural population data Lars Buesing∗ , Jakob H. Macke∗,† , Maneesh Sahani Gatsby Computational Neuroscience Unit Add to Reading List Source URL: www.mackelab.org Language: English - Date: 2016-08-04 15:02:44 Statistics Statistical theory Estimation theory Actuarial science Expectationmaximization algorithm Missing data Maximum likelihood estimation Variational Bayesian methods Linear regression Normal distribution Covariance Regression analysis
17	Learning stick-figure models using nonparametric Bayesian priors over trees Edward W. Meeds, David A. Ross, Richard S. Zemel, and Sam T. Roweis Department of Computer Science University of Toronto {ewm, dross, zemel, row Add to Reading List Source URL: www.cs.toronto.edu Language: English - Date: 2008-08-08 22:07:31 Statistics Probability Statistical theory Probability distributions Statistical models Bayesian statistics Stochastic processes Graphical models Bayesian network Gibbs sampling Dirichlet process Mixture model
18	A Short Introduction to Probabilistic Soft Logic Angelika Kimmig1,2 , Stephen H. Bach1 , Matthias Broecheler3 , Bert Huang1 , Lise Getoor1 1 University of Maryland, 2 KU Leuven, 3 Aurelius LLC Add to Reading List Source URL: stephenbach.net Language: English - Date: 2013-06-10 18:15:10 Logic Philosophy Abstraction Bayesian statistics Markov networks Reasoning Model theory Semantics Probabilistic soft logic Markov logic network Probabilistic logic Statistical relational learning
19	Quantifying Generalization in Linearly Weighted Neural Networks (Short title: Quantifying Generalization) Martin Anthony1 and Sean B. Holden2 Abstract Add to Reading List Source URL: www.maths.lse.ac.uk Language: English - Date: 2000-04-03 14:52:08 Machine learning Learning Artificial intelligence Computational learning theory Computational statistics Statistical classification Computational neuroscience VC dimension Artificial neural network Probably approximately correct learning Probability distribution Uniform convergence
20	Better Classifier Chains for Multi-label Classification Jesse Read, Fernando P´erez Cruz Department of Signal Theory and Communications Universidad Carlos III Madrid, Spain Add to Reading List Source URL: users.ics.aalto.fi Language: English - Date: 2011-07-21 06:05:16 Machine learning Classifier chains Statistical classification Multi-label classification Classifier Supervised learning Cognition Information

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