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Cross-validation
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91	ASSIGNMENT 12. VALIDATING THE REGRESSION MODEL 1. Run geosa11.m, selecting part 2 (assignment 12) from the opening menu. This selection specifies multiple linear regression with cross-validation. The difference from assi Add to Reading List Source URL: www.ltrr.arizona.edu Language: English - Date: 2015-04-10 12:59:37
92	pi Cholesky: Polynomial Interpolation of Multiple Cholesky Factors for Efficient Approximate Cross-Validation Da Kuang Raffay Hamid Add to Reading List Source URL: arxiv.org Language: English - Date: 2014-04-02 20:44:37
93	Sensitivity and Uncertainty Pre-analysis of the FNG Copper 14 MeV DT Benchmark for Fusion Relevant Cross-Section Validation I. Kodeli Jožef Stefan Institute Jamova cestaLjubljana, Slovenia Add to Reading List Source URL: www.djs.si Language: English - Date: 2015-02-07 16:48:02
94	Plasma and Fusion Research: Regular Articles Volume 8, Imaging Bolometers for Visualization of Plasma Radiation and Cross-Validation of Three Dimensional Impurity Transport Models Add to Reading List Source URL: www.nifs.ac.jp Language: English - Date: 2014-03-13 20:08:32
95	Testing fit patterns with polynomial regression models Felix Schönbrodt Ludwig-Maximilians-Universität München, Germany RA FT Add to Reading List Source URL: www.psy.lmu.de Language: English - Date: 2015-03-31 02:51:02 Akaike information criterion Polynomial regression Coefficient of determination Degrees of freedom Curve fitting Cross-validation Model selection Nonparametric regression Statistics Regression analysis Linear regression
96	Group-Based criminal trajectory analysis using cross-validation criteria J.D. Nielsen1 , J.S. Rosenthal2 , Y. Sun3 , D.M. Day4 , I. Bevc5 , and T. Duchesne6 (Last revised July 30, 2012.) Add to Reading List Source URL: probability.ca Language: English - Date: 2012-07-30 21:29:52 Statistical models Cluster analysis Categorical data Latent class model Bayesian information criterion Akaike information criterion Cross-validation Mixture model Statistics Model selection Machine learning
97	An ensemble of classiﬁers approach with multiple sources of information Roberto Santana, Concha Bielza, Pedro Larrañaga Departamento de Inteligencia Artiﬁcial, Universidad Politécnica de Madrid roberto.santana@upm. Add to Reading List Source URL: www.cis.hut.fi Language: English - Date: 2012-01-05 06:58:35 Machine learning Econometrics Covariance and correlation Correlation and dependence Feature selection Decision tree learning Cross-validation Logistic regression Statistics Regression analysis Model selection
98	B ARCELONA G RADUATE S CHOOL OF E CONOMICS - D ATA S CIENCE Random Forest Cover Type Prediction Jéssica Leal, Tim Kreienkamp and Philipp Schmidt Add to Reading List Source URL: dl.dropboxusercontent.com Language: English Ensemble learning Decision trees Regression analysis Random forest Cross-validation Overfitting Gradient boosting Boosting Decision tree learning Machine learning Statistics Computational statistics
99	Uncertainty Handling in Model Selection for Support Vector Machines Tobias Glasmachers and Christian Igel Institut f¨ ur Neuroinformatik, Ruhr-Universit¨ at Bochum, Germany Add to Reading List Source URL: image.diku.dk Language: English - Date: 2011-08-26 07:00:39 Stochastic optimization Statistical classification CMA-ES Computational statistics Cross-validation Support vector machine Mathematical optimization Supervised learning Stability Statistics Machine learning Evolutionary algorithms
100	Observations of internal model validation During its recent internal model review work the PRA has observed varying approaches to the role of validation in internal models. In order to assist firms with preparations for Add to Reading List Source URL: www.bankofengland.co.uk Language: English - Date: 2015-03-17 13:08:18 Quality Validation Ethology Economic model Conceptual model Philosophy of mind Statistics Cross-validation Verification and validation Pharmaceutical industry Validity Clinical research

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