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Statistical learning theory
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#	Item
291	HORSES: Hexagonal Operator for Regression with Shrinkage and Equality Selection Woncheol Jang, Donghyeon Yu, Johan Lim* Seoul National University, Seoul, Korea wjang,bunguji, Nicole Lazar Add to Reading List Source URL: www.statistics.gov.hk Language: English - Date: 2013-08-22 04:39:19 Estimation theory Statistical theory Machine learning Regularization Shrinkage Feature selection Elastic net regularization Statistics Regression analysis Econometrics
292	Learning Influence Probabilities In Social Networks Amit Goyal Francesco Bonchi Laks V. S. Lakshmanan Add to Reading List Source URL: www.wsdm-conference.org Language: English - Date: 2009-12-30 01:37:52 Statistical models Graph theory Bayesian statistics Probability and statistics Networks Markov processes Method of conditional probabilities Statistics Graphical models Connectivity
293	Interpolants as Classifiers? Rahul Sharma1 , Aditya V. Nori2 , and Alex Aiken1 1 Stanford University, {sharmar, aiken}@stanford.edu Microsoft Research India, Add to Reading List Source URL: theory.stanford.edu Language: English - Date: 2012-04-12 00:53:40 Mathematics Machine learning Abstract algebra Linear algebra Support vector machine Margin classifier Interpolation Vector space Polynomial Algebra Statistics Statistical classification
294	Department of Statistics STAT371 E1: Statistical Theory STAT810 E1: Statistical Theory Unit Outline Add to Reading List Source URL: stat.mq.edu.au Language: English - Date: 2014-11-05 17:22:27 Probability and statistics Statistical theory Design of experiments Distance education E-learning Probability theory Probability Asymptotic theory Statistical hypothesis testing Information Science Statistics
295	Theoretical Computer Science – 174 www.elsevier.com/locate/tcs Foreword This special issue contains six of the 21 regular and 3 invited papers presented at Add to Reading List Source URL: www-alg.ist.hokudai.ac.jp Language: English - Date: 2014-03-14 14:22:29 Science Machine learning Learning theory Inductive inference Statistical inference Algorithmic learning theory Algorithm Complexity Kolmogorov complexity Applied mathematics Theoretical computer science Computational learning theory
296	Theoretical Computer Science–2 www.elsevier.com/locate/tcs Preface This special volume contains the revised and expanded version of nine of the 28 Add to Reading List Source URL: www-alg.ist.hokudai.ac.jp Language: English - Date: 2014-03-14 14:24:09 Science Knowledge Inductive inference Inductive reasoning Inference Statistical inference Algorithmic learning theory Learnability Logic programming Computational learning theory Theoretical computer science Applied mathematics
297	Bias-Variance Tradeoffs in Program Analysis Rahul Sharma Aditya V. Nori Alex Aiken Add to Reading List Source URL: theory.stanford.edu Language: English - Date: 2013-11-07 17:01:23 Computational learning theory Hypothesis testing Statistical tests Overfitting VC dimension Cross-validation Variance Abstract interpretation Probably approximately correct learning Statistics Machine learning Statistical inference
298	video demo End-User Web Scraping: Google Scholar Edition Sarah Chasins Add to Reading List Source URL: www.icir.org Language: English - Date: 2014-12-10 15:02:23 Computational learning theory Year of birth missing Statistical classification Theoretical computer science Vladimir Vapnik Support vector machine Vapnik–Chervonenkis theory Structural risk minimization Machine learning Statistics Artificial intelligence
299	Learning Stochastic OT Grammars: A Bayesian approach using Data Augmentation and Gibbs Sampling Ying Lin∗ Department of Linguistics University of California, Los Angeles Los Angeles, CA 90095 Add to Reading List Source URL: dingo.sbs.arizona.edu Language: English - Date: 2005-05-03 02:03:46 Markov chain Monte Carlo Markov models Estimation theory Gibbs sampling Normal distribution Maximum likelihood Optimality theory Sampling distribution Random variable Statistics Statistical theory Probability and statistics
300	Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling Feng Jiao University of Waterloo Abstract We present a new semi-supervised training Add to Reading List Source URL: webdocs.cs.ualberta.ca Language: English - Date: 2006-12-28 15:24:48 Statistical theory Estimation theory Conditional random field Theoretical computer science Supervised learning Kullback–Leibler divergence Semi-supervised learning Expectation–maximization algorithm Conditional entropy Statistics Machine learning Information theory

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