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Supervised learning
Results: 1557

#	Item
291	An Empirical Comparison of Supervised Learning Algorithms Rich Caruana Alexandru Niculescu-Mizil Department of Computer Science, Cornell University, Ithaca, NYUSA Add to Reading List Source URL: www.niculescu-mizil.org Language: English - Date: 2008-11-22 14:04:17 Ensemble learning Bayesian statistics Neural networks Supervised learning Support vector machine Naive Bayes classifier Boosting Random forest Calibration Statistics Machine learning Statistical classification
292	Call for Participation: Beyond correlations: Developments in supervised learning algorithms for spiking neural networks Workshop at ICANN 2011, Helsinki, , Add to Reading List Source URL: www.cis.hut.fi Language: English - Date: 2011-05-12 09:25:10 Computational statistics Cybernetics Science Neuroscience Artificial neural network Spiking neural network Reinforcement learning Spike-timing-dependent plasticity Backpropagation Neural networks Machine learning Computational neuroscience
293	Model Selection in Compositional Spaces by Roger Baker Grosse B.S., Stanford UniversityM.S., Stanford University (2008) Add to Reading List Source URL: www.cs.toronto.edu Language: English - Date: 2015-06-03 15:15:50 Bayesian statistics Statistical models Generative model Bayes factor Variational Bayesian methods Variational message passing Marginal likelihood Supervised learning Gibbs sampling Statistics Machine learning Probability and statistics
294	Probabilistic Classification of Image Regions using Unsupervised and Supervised Learning Add to Reading List Source URL: www.sanjivk.com Language: English - Date: 2010-06-01 18:49:42 Pattern recognition Segmentation Mixture model Minimum description length Generative model Unsupervised learning Expectation–maximization algorithm Statistical classification Supervised learning Statistics Machine learning Cluster analysis
295	Yarin Gal Education 2012–2016 PhD, Information Engineering, Machine Learning Group, University of Cambridge, Cambridge, UK, (supervised by Prof Zoubin Ghahramani). Supported by the Google Doctoral fellowship; Qualcomm Add to Reading List Source URL: mlg.eng.cam.ac.uk Language: English - Date: 2015-06-06 07:55:27
296	Leveraging In-Batch Annotation Bias for Crowdsourced Active Learning Honglei Zhuang Joel Young Add to Reading List Source URL: web.engr.illinois.edu Language: English - Date: 2014-12-10 22:11:36 Learning Supervised learning Annotation Active learning Statistical classification Word-sense disambiguation Batch file Machine learning Artificial intelligence Linguistics
297	Semi-Supervised Learning on Riemannian Manifolds Mikhail Belkin∗ , Partha Niyogi † University of Chicago, Department of Computer Science Add to Reading List Source URL: www2.imm.dtu.dk Language: English - Date: 2009-08-14 05:09:09
298	Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition Marc’Aurelio Ranzato, Fu Jie Huang, Y-Lan Boureau, Yann LeCun Courant Institute of Mathematical Sciences, New York Universi Add to Reading List Source URL: www.cs.toronto.edu Language: English - Date: 2009-08-08 18:43:08 Neural networks Learning Statistical classification Pattern recognition Supervised learning Autoencoder Unsupervised learning Caltech 101 Support vector machine Machine learning Statistics Artificial intelligence
299	Didacticiel - Etudes de cas R.R. Subject A Multilayer Perceptron for a classification task (neural network): comparison of Add to Reading List Source URL: eric.univ-lyon2.fr Language: English - Date: 2006-03-31 12:17:50 Learning Perceptron Multilayer perceptron Supervised learning Support vector machine Neural networks Statistics Cybernetics
300	Who Do I Look Like? Determining Parent-Offspring Resemblance via Gated Autoencoders Afshin Dehghan, Enrique G. Ortiz, Ruben Villegas, Mubarak Shah Center for Research in Computer Vision, University of Central Florida {ad Add to Reading List Source URL: crcv.ucf.edu Language: English - Date: 2014-04-20 10:47:37 Probability and statistics Discriminative model Kinship Supervised learning The Offspring Machine learning Learning Generative model

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