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Feature selection
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#	Item
431	Data Science with R Decision Trees with Rattle [removed] 9th June 2014 Visit http://onepager.togaware.com/ for more OnePageR’s. Add to Reading List Source URL: handsondatascience.com Language: English - Date: 2014-06-09 00:23:58 Decision theory Decision tree Data mining Accuracy and precision GUI widget Form Feature selection Rattle GUI Statistics Model selection Dimension reduction
432	Dimensionality Reduction for Data Mining - Techniques, Applications and Trends Lei Yu Binghamton University Add to Reading List Source URL: www.cs.binghamton.edu Language: English - Date: 2007-02-23 12:11:53 Multivariate statistics Microarrays Computer vision Feature extraction Curse of dimensionality Data mining Feature selection Affymetrix Feature vector Statistics Dimension reduction Machine learning
433	Functions that Demonstrate the Sampling Properties of Variable Selection John Maindonald October 7, 2013 The function varselect() in the leaps package can be used for variable selection. Available appraoches are forward, Add to Reading List Source URL: cran.r-project.org Language: English - Date: 2014-07-02 09:39:05 Variables Elementary mathematics Econometrics Dimension reduction Feature selection Function Stepwise regression Instrumental variable Mathematics Statistics Regression analysis
434	R in the Midst of Exploding Stars: Distributed, Time-Domain Transient Classification ? Thomas J. Fuchs1,2, , Michael Turmon1 , Matthew J. Graham2 , Ciro Donalek2 , Ashish Mahabal2 Andrew J. Drake2 , S. George Djorgovski2 Add to Reading List Source URL: user2014.stat.ucla.edu Language: English - Date: 2014-06-28 21:51:12 Feature selection Model selection Random forest California Institute of Technology Astronomy Machine learning Statistics Dimension reduction
435	DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition arXiv:1310.1531v1 [cs.CV] 6 Oct[removed]Jeff Donahue∗ , Yangqing Jia∗ , Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell Add to Reading List Source URL: arxiv.org Language: English - Date: 2013-10-07 20:25:05 Cross-validation Support vector machine Perceptual learning Statistics Model selection One-shot learning
436	Creating a pivot table and chart from IAPP Extracts for Data Analysis and Reporting Purposes This factsheet will feature two examples, based on:  A Chemical Treatment extract, and  A Site Selection extract Add to Reading List Source URL: www.for.gov.bc.ca Language: English - Date: 2012-03-26 15:22:46 Pivot table Pivot chart Herbicide Spreadsheet Microsoft Excel Computer-aided audit tools Insert Application software Software Computing Microsoft Office
437	2013 IEEE International Conference on Big Data Feature Selection Strategies for Classifying High Dimensional Astronomical Data Sets Ciro Donalek, S. G. Djorgovski, Ashish A. Mahabal, Matthew J. Graham, Andrew J. Drake Add to Reading List Source URL: www.kiss.caltech.edu Language: English - Date: 2014-04-29 12:54:28 K-nearest neighbor algorithm Feature selection Supervised learning Linear classifier Statistical classification Kepler Least-angle regression RR Lyrae variable Pattern recognition Statistics Machine learning Artificial intelligence
438	Non-Linear Monte-Carlo Search in Civilization II S.R.K. Branavan David Silver * Computer Science and Artificial Intelligence Laboratory Add to Reading List Source URL: people.csail.mit.edu Language: English - Date: 2011-06-14 21:02:58 Markov decision process Algorithm Feature selection Monte Carlo method Statistics Probability and statistics Reinforcement learning
439	Package ‘hddplot’ July 2, 2014 Type Package Title Use known groups in high-dimensional data to derive scores for plots Version 0.56 Date[removed] Add to Reading List Source URL: cran.r-project.org Language: English - Date: 2014-07-02 12:06:59 Singular value decomposition Statistical inference Linear discriminant analysis Statistical classification Principal component analysis Feature selection Cross-validation Matrix Statistical hypothesis testing Statistics Multivariate statistics Model selection
440	Joint Feature Selection for Object Detection and Recognition Jerod J. Weinman, Allen Hanson, and Erik Learned-Miller {weinman,hanson,elm}@cs.umass.edu Department of Computer Science University of Massachusetts-Amherst Am Add to Reading List Source URL: vis-www.cs.umass.edu Language: English - Date: 2008-01-02 15:36:37 Artificial intelligence Image processing Machine learning Object recognition Linear discriminant analysis Cross-validation Template matching One-shot learning Boosting methods for object categorization Computer vision Statistics Vision

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