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Cross-validation
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#	Item
231	Measuring forecast accuracy Rob J Hyndman Monday, 31 March 2014 Everyone wants to know how accurate their forecasts are. Does your forecasting method give good forecasts? Are they better than the competitor methods? Add to Reading List Source URL: robjhyndman.com Language: English - Date: 2014-05-23 21:35:02 Forecasting Forecast error Mean absolute scaled error Cross-validation Mean absolute percentage error Mean absolute error Calculating demand forecast accuracy Ensemble forecasting Statistics Statistical forecasting Time series analysis
232	Chapter 8 lecture questions Q1: After you completed the 5-fold cross-validation, some new data have become available. How would you make predictions with the new data? Answer: With 5-fold cross-validation, you built 5 mo Add to Reading List Source URL: www.ocgy.ubc.ca Language: English - Date: 2013-11-08 19:43:31 Monte Carlo methods Resampling Bootstrapping Cross-validation Mode Bootstrap aggregating Statistics Statistical inference Computational statistics
233	8.6 Bayesian neural networks (BNN) [Book, Sect[removed]While cross-validation allows one to find the weight penalty parameters which would give the model good generalization capability, the separation of the data record in Add to Reading List Source URL: www.ocgy.ubc.ca Language: English - Date: 2013-11-09 02:48:24 Machine learning Computational science Econometrics Cross-validation Resampling Bootstrapping Bootstrap aggregating Numerical weather prediction Overfitting Statistics Statistical inference Computational statistics
234	SlimPLS: A Method for Feature Selection in Gene Expression-Based Disease Classification Michael Gutkin1, Ron Shamir1, Gideon Dror2 1 Blavatnik school of Computer Science, Tel Aviv University, Tel Aviv, Israel, 2 School Add to Reading List Source URL: www2.mta.ac.il Language: English* - Date: 2010-08-18 18:53:13 Support vector machine Naive Bayes classifier Classifier Binary classification Supervised learning Feature selection Linear discriminant analysis Cross-validation Random subspace method Statistics Machine learning Statistical classification
235	TRECVID 2012 GENIE: Multimedia Event Detection and Recounting A. G. Amitha Perera1 , Sangmin Oh1 , Megha Pandey1 , Tianyang Ma1 , Anthony Hoogs1 , Arash Vahdat2 , Kevin Cannons2 , Hossein Hajimirsadeghi2 , Greg Mori2 , S Add to Reading List Source URL: www-nlpir.nist.gov Language: English - Date: 2013-01-22 17:44:03 Cross-validation Model selection Support vector machine Histogram Statistics Machine learning Statistical classification
236	perClass 4.5 cheat-sheet Subsets! Detection! Add to Reading List Source URL: perclass.com Language: English - Date: 2015-03-17 10:16:32 Receiver operating characteristic Naive Bayes classifier Support vector machine Random forest Confusion matrix Cross-validation Classifier Linear discriminant analysis Statistics Statistical classification Machine learning
237	Image de-noising by integer wavelet transforms and generalized cross validation Maarten Jansen Geert Uytterhoeven Adhemar Bultheel Add to Reading List Source URL: www.cs.kuleuven.be Language: English - Date: 2010-05-13 11:47:10 Mathematics Wavelet Lifting scheme Discrete wavelet transform Thresholding Multiresolution analysis Continuous wavelet transform Wavelets Mathematical analysis Signal processing
238	Combining Knowledge and Data Driven Insights for Identifying Risk Factors using Electronic Health Records Jimeng Sun, Jianying Hu, Dijun Luo, Marianthi Markatou, Fei Wang, Shahram Edabollahi IBM T.J. Watson Research Cent Add to Reading List Source URL: www.research.ibm.com Language: English - Date: 2012-08-13 11:38:22 Medicine Model selection Heart diseases Heart Data mining Cross-validation Comorbidity Coronary artery disease Myocardial infarction Health Statistics Aging-associated diseases
239	Hydrol. Earth Syst. Sci., 14, 2527–2544, 2010 www.hydrol-earth-syst-sci.netdoi:hess © Author(sCC Attribution 3.0 License. Hydrology and Add to Reading List Source URL: www.hydrol-earth-syst-sci.net Language: English - Date: 2014-12-04 02:44:33 Interpolation Geostatistics Generalized extreme value distribution Estimation theory Kriging Linear regression Multivariate interpolation Spatial analysis Cross-validation Statistics Regression analysis Econometrics
240	EASY AS CBA: A SIMPLE PROBABILISTIC MODEL FOR TAGGING MUSIC Matthew D. Hoffman Dept. of Computer Science Princeton University mdhoffma at cs.princeton.edu Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2015-03-12 00:16:21 Natural language processing Vector quantization Music information retrieval Cross-validation Science Information science Information retrieval Statistics

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