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Feature selection
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#	Item
231	Ad Click Prediction: a View from the Trenches H. Brendan McMahan, Gary Holt, D. Sculley, Michael Young, Dietmar Ebner, Julian Grady, Lan Nie, Todd Phillips, Eugene Davydov, Daniel Golovin, Sharat Chikkerur, Dan Liu, Mart Add to Reading List Source URL: static.googleusercontent.com Language: English - Date: 2013-08-12 13:25:40 Cross-validation Poisson regression Pattern recognition Feature selection Least squares Regression analysis Regularization Gradient boosting Book:Machine Learning - The Complete Guide Statistics Machine learning Supervised learning
232	The Main Menu THE MAIN MENU The main menu is the jumping-off point for every feature in the system. By making a selection from one of the numbered choices, any of the programs that comprise the whole system will be load Add to Reading List Source URL: www.datasmithpayroll.com Language: English - Date: 2008-09-16 20:41:19 Paycheck Computer file Business Payroll service bureau Employment compensation Expense Payroll
233	Noname manuscript No. (will be inserted by the editor) Unsupervised Feature Construction for Improving Data Representation and Semantics Marian-Andrei Rizoiu · Julien Velcin · Add to Reading List Source URL: www.rizoiu.eu Language: English - Date: 2013-10-04 10:30:16 Learning Supervised learning Feature selection Unsupervised learning Pattern recognition Feature Algorithm Machine learning Artificial intelligence Statistics
234	Machine Learning, 45, 5–32, 2001 c 2001 Kluwer Academic Publishers. Manufactured in The Netherlands. Random Forests LEO BREIMAN Add to Reading List Source URL: www.islab.ntua.gr Language: English - Date: 2015-02-05 08:23:19 Random forest AdaBoost Boosting Variance Feature selection Correlation and dependence Statistical classification Cross-validation Random subspace method Machine learning Statistics Ensemble learning
235	5th AACTA AWARDS MATERIALS CHECKLIST FEATURE FILM – PRE-SELECTION The following items must be uploaded as part of your online entry:   Add to Reading List Source URL: aacta.org Language: English - Date: 2015-03-31 23:47:52 Computer graphics AACTA Awards ISO standards Tagged Image File Format QuickTime Australian Academy of Cinema and Television Arts JPEG Toronto International Film Festival Graphics file formats Computing Software
236	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
237	Informative Feature Selection for Object Recognition via Sparse PCA∗ Nikhil Naikal, Allen Y. Yang, and S. Shankar Sastry Department of EECS, University of California, Berkeley {nnaikal, yang, sastry}@eecs.berkeley.edu Add to Reading List Source URL: www.eecs.berkeley.edu Language: English - Date: 2011-06-10 12:51:40 Mathematics Singular value decomposition Data analysis Computer vision Principal component analysis Eigenvalues and eigenvectors Object recognition Bundle adjustment Matrix Statistics Algebra Multivariate statistics
238	A Parameterized Complexity Analysis of Combinatorial Feature Selection Problems? Vincent Froese, Ren´e van Bevern, Rolf Niedermeier, and Manuel Sorge Institut f¨ ur Softwaretechnik und Theoretische Informatik, TU Berli Add to Reading List Source URL: fpt.akt.tu-berlin.de Language: English - Date: 2013-06-19 10:42:56 Complexity classes Mathematical optimization Structural complexity theory NP-complete problems Parameterized complexity NP-complete P versus NP problem NP FO Theoretical computer science Computational complexity theory Applied mathematics
239	Improving the Accuracy of Decision Tree Induction by Feature Pre-Selection Add to Reading List Source URL: www.data-mining-tutorial.de Language: English - Date: 2009-10-09 07:20:34 Decision trees Dimension reduction Feature selection Model selection Search algorithms C4.5 algorithm Algorithm Pattern recognition K-nearest neighbor algorithm Machine learning Artificial intelligence Mathematics
240	C2FS: An Algorithm for Feature Selection in Cascade Neural Networks Lars Backstrom Rich Caruana Computer Science Add to Reading List Source URL: www.cs.cornell.edu Language: English - Date: 2006-09-13 16:02:40 Cybernetics Early stopping Backpropagation Feature selection Artificial neural network Algorithm Artificial neuron Feature Overfitting Neural networks Statistics Machine learning

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