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Cross-validation
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#	Item
211	Ivans Lubenko & Andrew Ker lubenko @ comlab.ox.ac.uk adk @ comlab.ox.ac.uk Oxford University Computing Laboratory Add to Reading List Source URL: www.cs.ox.ac.uk Language: English - Date: 2011-01-25 19:29:51 Support vector machine Steganalysis Binary classification Cross-validation Logistic regression Statistics Machine learning Statistical classification
212	Multi-Task Bayesian Optimization Kevin Swersky Department of Computer Science University of Toronto Add to Reading List Source URL: papers.nips.cc Language: English - Date: 2014-11-25 16:20:14 Machine learning Estimation theory Mathematical optimization Operations research Hyperparameter Global optimization Cross-validation Bayesian inference Likelihood function Statistics Bayesian statistics Statistical theory
213	Java SE 7 Preview Alex Miller Blog: http://tech.puredanger.com Twitter: http://twitter.com/puredanger Add to Reading List Source URL: puredanger.com Language: English - Date: 2009-02-04 22:36:59 Computing platforms Cross-platform software Java enterprise platform Java Management Extensions Java Community Process Java Platform Micro Edition Java Platform Standard Edition Bean Validation Java concurrency Computing Java platform Java specification requests
214	Generalized Cross Validation in variable selection with and without shrinkage Maarten Jansen Universit´e Libre de Bruxelles, departments of Mathematics and Computer Science January 19, 2015 Add to Reading List Source URL: homepages.ulb.ac.be Language: English - Date: 2015-01-19 05:59:00 Bias Bias of an estimator Efficient estimator Normal distribution Variance Statistics Statistical theory Estimation theory
215	IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 7, NO. 2, APRILFrom Blind to Quantitative Steganalysis Tomáš Pevný, Jessica Fridrich, Member, IEEE, and Andrew D. Ker, Member, IEEE Add to Reading List Source URL: www.cs.ox.ac.uk Language: English - Date: 2012-04-21 10:14:54 Econometrics Estimation theory Statistical theory Steganalysis Digital watermarking Jessica Fridrich JPEG Regression analysis Cross-validation Statistics Cryptography Steganography
216	FLAX: Systematic Discovery of Client-side Validation Vulnerabilities in Rich Web Applications Prateek Saxena§ Steve Hanna§ Pongsin Poosankam‡§ Dawn Song§ Add to Reading List Source URL: www.andrew.cmu.edu Language: English - Date: 2013-09-09 05:44:56 Scripting languages Software testing Ajax JavaScript Cross-platform software Cross-site scripting JSON Cross-site request forgery Code injection Computing Computer security Cyberwarfare
217	Discriminatively Trained Markov Model for Sequence Classification Oksana Yakhnenko, Adrian Silvescu, Vasant Honavar Artificial Intelligence Research Laboratory Center for Computational Intelligence Learning and Discovery Add to Reading List Source URL: www.cs.iastate.edu Language: English - Date: 2005-10-29 21:29:16 Graphical models Bayesian statistics Statistical models Generative model Naive Bayes classifier Support vector machine Discriminative model Cross-validation Markov random field Statistics Machine learning Statistical classification
218	Forecasting a Moving Target: Ensemble Models for ILI Case Count Predictions Prithwish Chakraborty Aravindan Mahendiran Sumiko R Mekaru§ ∗† Add to Reading List Source URL: people.cs.vt.edu Language: English - Date: 2014-04-11 14:34:12 Econometrics Categorical data Estimation theory Linear regression Kalman filter Logistic regression Cross-validation Flu pandemic Count data Statistics Regression analysis Data analysis
219	2009 Ninth IEEE International Conference on Data Mining Combining Super-structuring and Abstraction on Sequence Classification Adrian Silvescu Yahoo! Labs Sunnyvale, CA Add to Reading List Source URL: www.cs.iastate.edu Language: English - Date: 2010-03-12 19:01:56 Science Model selection Statistical classification Computational linguistics Natural language processing Support vector machine Abstraction N-gram Cross-validation Statistics Artificial intelligence Machine learning
220	Appears in From Statistics to Neural Networks: Theory and Pattern Recognition Applications, V. Cherkassky, J.H. Friedman and H. Wechsler (eds.), NATO ASI Series F, Springer-VerlagPrediction Risk and Architecture Add to Reading List Source URL: www.smartquant.com Language: English - Date: 2014-02-08 06:27:50 Regression analysis Model selection Least squares Cross-validation Resampling Pruning Overfitting Training set Estimation theory Statistics Machine learning Statistical inference

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