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Statistical learning theory
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#	Item
401	Robert Williamson Digitally signed by Robert Williamson DN: cn=Robert Williamson, o=NICTA, ou, email=Bob.Williamson@nicta. Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2009-08-08 03:02:47 Knowledge Computational learning theory Concept learning Supervised learning Statistical inference Bayesian probability Loss function Statistical classification Data mining Machine learning Statistics Science
402	Notes 09 Statistical Machine Learning Probabilistic Learning Instructor: Justin Domke Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2011-11-07 14:35:35 Maximum likelihood Fisher information Normal distribution Likelihood function Bias of an estimator Multivariate normal distribution Loss function Kullback–Leibler divergence Statistics Estimation theory Statistical theory
403	Spatial Priors for Part-Based Recognition using Statistical Models David Crandall1 Cornell University [removed] Pedro Felzenszwalb Add to Reading List Source URL: vision.soic.indiana.edu Language: English - Date: 2014-08-03 00:38:07 Image processing Artificial intelligence Estimation theory Object recognition Maximum likelihood Edge detection One-shot learning Scale-invariant feature transform Computer vision Vision Imaging
404	Le Cam meets LeCun: Deficiency and Generic Feature Learning Brendan van Rooyen Robert C. Williamson BRENDAN . VANROOYEN @ ANU . EDU . AU BOB . WILLIAMSON @ ANU . EDU . AU Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2014-03-18 00:04:09 Econometrics Loss function Sufficient statistic Trigonometry Complex analysis Polar coordinate system Symbol Statistics Statistical theory Decision theory
405	Notes 4 Statistical Machine Learning Linear Methods Instructor: Justin Domke Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2011-11-07 14:35:34 Econometrics Estimation theory Least squares Least absolute deviations Linear regression Regularization Elastic net regularization Tikhonov regularization Support vector machine Statistics Regression analysis Mathematical optimization
406	VC-dimension for characterizing classifiers Note to other teachers and users of these slides. Andrew would be delighted if you found this source material useful in Add to Reading List Source URL: www.autonlab.org Language: English - Date: 2008-07-07 16:16:22 Statistics Dimension Statistical classification VC dimension Shattered set Vector space Algebra Mathematics Computational learning theory
407	Online Learning with Kernels Jyrki Kivinen Alex J. Smola Robert C. Williamson Research School of Information Sciences and Engineering Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2005-03-22 08:39:23 Statistical theory Computational statistics Neural networks Computational neuroscience Support vector machine Stochastic gradient descent Perceptron Loss function Robust statistics Statistics Machine learning Econometrics
408	Machine Learning using Hyperkernels Cheng Soon Ong [removed] Alexander J. Smola [removed] Add to Reading List Source URL: www.ong-home.my Language: English - Date: 2013-12-12 02:50:01 Statistical classification Mathematical analysis Operator theory Support vector machine Positive-definite kernel Reproducing kernel Hilbert space Linear map Symbol Differences between codices Sinaiticus and Vaticanus Hilbert space Algebra Mathematics
409	Weakly Supervised Learning of Part-Based Spatial Models for Visual Object Recognition David J. Crandall and Daniel P. Huttenlocher Cornell University, Ithaca, NY 14850, USA, {crandall,dph}@cs.cornell.edu Add to Reading List Source URL: vision.soic.indiana.edu Language: English - Date: 2014-08-03 00:38:09 Machine learning Artificial intelligence Maximum likelihood Expectation–maximization algorithm Object recognition Supervised learning Markov random field Constellation model One-shot learning Statistics Estimation theory Statistical theory
410	Weakly Supervised Learning of Part-Based Spatial Models for Visual Object Recognition David J. Crandall and Daniel P. Huttenlocher Cornell University, Ithaca, NY 14850, USA, {crandall,dph}@cs.cornell.edu Add to Reading List Source URL: www.cs.cornell.edu Language: English - Date: 2006-02-18 10:40:24 Machine learning Artificial intelligence Maximum likelihood Expectation–maximization algorithm Object recognition Supervised learning Markov random field Constellation model One-shot learning Statistics Estimation theory Statistical theory

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