Multivariate

Results: 4641



#Item
161Semi-Supervised Dimensionality Reduction for Analyzing High-Dimensional Data with Constraints Su Yan∗∗ IBM Almaden Research Center, 650 Harry Rd San Jose, CA 95120, USA  Sofien Bouaziz

Semi-Supervised Dimensionality Reduction for Analyzing High-Dimensional Data with Constraints Su Yan∗∗ IBM Almaden Research Center, 650 Harry Rd San Jose, CA 95120, USA Sofien Bouaziz

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Source URL: sofienbouaziz.com

Language: English - Date: 2015-11-19 01:50:28
162DOI: j01783.x  COMPUTER GRAPHICS forum

DOI: j01783.x COMPUTER GRAPHICS forum

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Source URL: www.wisdom.weizmann.ac.il

Language: English - Date: 2013-10-23 08:31:15
163AMERICAN JOURNAL OF HUMAN BIOLOGY 23:796–Original Research Article Postnatal Ontogeny of Tibia and Femur Form in Two Human Populations: A Multivariate Morphometric Analysis

AMERICAN JOURNAL OF HUMAN BIOLOGY 23:796–Original Research Article Postnatal Ontogeny of Tibia and Femur Form in Two Human Populations: A Multivariate Morphometric Analysis

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Source URL: theoretical.univie.ac.at

Language: English - Date: 2013-03-28 11:03:27
164Optimal Kernels for Unsupervised Learning Sepp Hochreiter and Klaus Obermayer Bernstein Center for Computational Neuroscience and Technische Universit¨at BerlinBerlin, Germany

Optimal Kernels for Unsupervised Learning Sepp Hochreiter and Klaus Obermayer Bernstein Center for Computational Neuroscience and Technische Universit¨at BerlinBerlin, Germany

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Source URL: www.bioinf.jku.at

Language: English - Date: 2013-09-23 08:37:52
165Tomographic Inversion using NURBS and MCMC Zenith Purisha and Samuli Siltanen Department of Mathematics and Statistics University of Helsinki Helsinki, Finland ,

Tomographic Inversion using NURBS and MCMC Zenith Purisha and Samuli Siltanen Department of Mathematics and Statistics University of Helsinki Helsinki, Finland ,

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Source URL: www.siltanen-research.net

Language: English - Date: 2013-11-07 07:17:29
166Manifold learning algorithms aim to recover the underlying lowdimensional parametrization of the data using either local or global features. It is however widely recognized that the low dimensional parametrizations will

Manifold learning algorithms aim to recover the underlying lowdimensional parametrization of the data using either local or global features. It is however widely recognized that the low dimensional parametrizations will

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Source URL: mmds-data.org

Language: English - Date: 2016-06-23 15:50:48
167

PDF Document

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Source URL: cs.brown.edu

Language: English - Date: 2015-10-08 18:02:35
168Source Separation as a By-Product of Regularization Sepp Hochreiter  Fakultat fur Informatik

Source Separation as a By-Product of Regularization Sepp Hochreiter Fakultat fur Informatik

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Source URL: www.bioinf.jku.at

Language: English - Date: 2013-01-23 02:19:45
169SPATIAL ORDERING OF VORONOI NETWORKS AND THEIR USE IN TERRAIN DATA BASE MANAGEMENT Christopher M. Gold College of Geographic Sciences P.O. Box 10, Lawrencetown

SPATIAL ORDERING OF VORONOI NETWORKS AND THEIR USE IN TERRAIN DATA BASE MANAGEMENT Christopher M. Gold College of Geographic Sciences P.O. Box 10, Lawrencetown

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Source URL: mapcontext.com

Language: English - Date: 2008-08-30 00:30:14
170Linking for the general diagnostic model Xueli Xu and Matthias von Davier Educational Testing Service, Princeton, New Jersey, United States This study analyzed National Assessment of Educational Progress (NAEP) reading d

Linking for the general diagnostic model Xueli Xu and Matthias von Davier Educational Testing Service, Princeton, New Jersey, United States This study analyzed National Assessment of Educational Progress (NAEP) reading d

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Source URL: ierinstitute.org

Language: English - Date: 2012-01-04 07:01:18