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Feature selection
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#	Item
151	TorinoFilmLab announces FrameWork 2013 selection TorinoFilmLab has the great pleasure to present the selection for the 6th edition of FrameWork. A total of 10 first or second feature film projects from all over the w Add to Reading List Source URL: www.torinofilmlab.it Language: English - Date: 2013-03-05 06:48:33
152	Feature Selection in Hierarchical Feature Spaces Petar Ristoski and Heiko Paulheim University of Mannheim, Germany Research Group Data and Web Science {petar.ristoski,heiko}@informatik.uni-mannheim.de Add to Reading List Source URL: dws.informatik.uni-mannheim.de Language: English - Date: 2015-04-21 05:23:35
153	Mining Emerging Patterns by Streaming Feature Selection Kui Yu1, 2, Wei Ding2, Dan A. Simovici2, and Xindong Wu✉1,3 1 Department of Computer Science, Hefei University of Technology, Hefei, 230009, China Department of C Add to Reading List Source URL: www.cs.umb.edu Language: English - Date: 2012-06-04 16:27:50
154	Best Full Length Feature: Jury Selection Add to Reading List Source URL: www.mglff.com Language: English - Date: 2015-05-15 13:26:19
155	Mapping phenotypes to networks of genetic markers: a min-cut solution to feature selection with sparsity and connectivity constraints Chlo´ e-Agathe Azencott with Dominik Grimm, Yoshinobu Kawahara, and Karsten Borgwardt Add to Reading List Source URL: cazencott.info Language: English - Date: 2014-12-14 10:09:36
156	Hybrid feature selection and SVM-based classification for mouse skin precancerous stages diagnosis from bimodal spectroscopy F. Abdat,1 M. Amouroux,1 Y. Guermeur,2 and W. Blondel1,∗ 1 Centre de Recherche en Automatique Add to Reading List Source URL: www.loria.fr Language: English - Date: 2011-12-24 07:06:07 Science Autofluorescence Raman spectroscopy Astronomical spectroscopy Ultraviolet–visible spectroscopy Discrete cosine transform Absorption spectroscopy Emission spectrum Infrared spectroscopy Physics Spectroscopy Chemistry
157	Automatic Detection of Craters in Planetary Images: An Embedded Framework Using Feature Selection and Boosting Wei Ding1 , Tomasz F. Stepinski2 , Lourenco Bandeira3 , Ricardo Vilalta4 Youxi Wu5 , Zhenyu Lu5 , and Tianyu Add to Reading List Source URL: www.cs.umb.edu Language: English - Date: 2010-09-05 18:04:21 Learning Computational statistics Lunar science Supervised learning Lunar craters AdaBoost Impact crater Boosting Müller Ensemble learning Machine learning Artificial intelligence
158	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
159	CS229 Lecture notes Andrew Ng Part VI Regularization and model Add to Reading List Source URL: see.stanford.edu Language: English - Date: 2007-12-11 16:41:19 Estimation theory Model selection Information theory Maximum likelihood Supervised learning Feature selection Kullback–Leibler divergence Regularization Mutual information Statistics Statistical theory Machine learning
160	Parallel Large Scale Feature Selection for Logistic Regression Sameer Singh, University of Massachusetts Amherst MA 01003 Add to Reading List Source URL: www.cs.cmu.edu Language: English - Date: 2009-01-28 18:57:39 Feature selection Logistic regression Random forest Stepwise regression Mathematical optimization Support vector machine Algorithm Statistics Regression analysis Iteratively reweighted least squares

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