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#	Item
411	Journal of Machine Learning Research[removed]N -N + 45 Submitted 8/00; Published Real Soon Prior Knowledge and Preferential Structures in Gradient Descent Learning Algorithms∗ Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2001-08-30 21:18:58 Stochastic gradient descent Statistics Differentiable manifold Riemannian manifold Supervised learning Gradient Mathematical analysis Mathematics Stochastic optimization
412	Machine Learning manuscript No. (will be inserted by the editor) Clustering: Science or Art?? Ulrike von Luxburg · Robert C. Williamson · Isabelle Guyon Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2011-02-21 00:49:16 Cluster analysis Data mining Geostatistics K-means clustering Hierarchical clustering Statistical classification Unsupervised learning Consensus clustering Document clustering Statistics Machine learning Data analysis
413	Integration of Visual and Inertial Information for Egomotion: a Stochastic Approach Justin Domke and Yiannis Aloimonos Computer Vision Laboratory, Dept. of Computer Science University of Maryland, College Park, MD 20742 Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2011-11-07 14:37:10 Relativity Astrodynamics Inertial frame of reference Physics Classical mechanics Introductory physics
414	Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2005-03-22 07:54:20
415	A Probabilistic Notion of Correspondence and the Epipolar Constraint Justin Domke and Yiannis Aloimonos Computer Vision Laboratory, Dept. of Computer Science University of Maryland, College Park, MD[removed]USA {domke,yian Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2011-11-07 14:37:14 Mathematical analysis Probability Fundamental matrix Epipolar geometry Normal distribution Statistics
416	Loss Functions Robert C. Williamson Abstract Vapnik described the “three main learning problems” of pattern recognition, regression estimation and density estimation. These are defined in terms of the loss functions Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2013-04-06 02:21:38 Statistical theory Decision theory Kullback–Leibler divergence Thermodynamics Loss function Bregman divergence Bayes estimator Divergence Distribution Statistics Geometry Mathematical analysis
417	Notes 4 Statistical Machine Learning Basis Expansions Instructor: Justin Domke Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2011-11-07 14:35:36 Least squares Perceptron Mathematics Regression analysis Function
418	Comparing Nearest Neighbor Algorithms in High-Dimensional Space Hendra Gunadi College of Engineering and Computer Science Australian National University Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2014-11-14 04:03:55 Mathematics Nearest neighbor search K-nearest neighbor algorithm Locality-sensitive hashing Ε-approximate nearest neighbor search Nearest-neighbor chain algorithm Information science Search algorithms Artificial intelligence
419	Video Genetics: A Case Study from YouTube John R. Kender Matthew L. Hill Apostol (Paul) Natsev Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2012-04-29 22:40:00 Family Television Visual arts Imaging Key frame Video 12-hour clock
420	Bayesian Online Learning for Multi-label and Multi-variate Performance Measures Xinhua Zhang University of Alberta [removed] Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2010-03-31 21:42:25 Support vector machine Supervised learning Sufficient statistic Bayes estimator Statistical classification Bayesian inference Kullback–Leibler divergence Mixture model Statistics Statistical theory Machine learning

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