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Unsupervised learning
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271	GEOSPATIOTEMPORAL DATA MINING IN AN EARLY WARNING SYSTEM FOR FOREST THREATS IN THE UNITED STATES F. M. Hoffman∗ , R. T. Mills† , J. Kumar‡ , S. S. Vulli§ W. W. Hargrove¶ Add to Reading List Source URL: www.climatemodeling.org Language: English - Date: 2010-07-09 14:58:19 Remote sensing Normalized Difference Vegetation Index Phenology EVI Cluster analysis K-means clustering Data mining Unsupervised learning Statistics Data analysis Machine learning
272	Int J Comput Vis DOI[removed]s11263[removed]Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words Juan Carlos Niebles · Hongcheng Wang · Li Fei-Fei Add to Reading List Source URL: vision.stanford.edu Language: English - Date: 2009-05-16 21:56:28 Latent Dirichlet allocation Probabilistic latent semantic analysis Topic model Expectation–maximization algorithm Causality Object categorization from image search Statistics Statistical natural language processing Machine learning
273	Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis Quoc V. Le, Will Y. Zou, Serena Y. Yeung, Andrew Y. Ng Computer Science Department and Department of Elec Add to Reading List Source URL: ai.stanford.edu Language: English - Date: 2011-04-07 02:30:19 Image processing Learning Vision Supervised learning Feature Unsupervised learning Machine learning Artificial intelligence Computer vision
274	Unsupervised Prediction Aggregation Alexandre Klementiev Johns Hopkins University Baltimore, MD[removed]removed] Add to Reading List Source URL: www.select.cs.cmu.edu Language: English - Date: 2009-11-10 02:20:14 Science Natural language processing Cybernetics Learning Aggregation Supervised learning Unsupervised learning Computational linguistics Machine learning Artificial intelligence
275	Unsupervised Feature Learning by Deep Sparse Coding arXiv:1312.5783v1 [cs.LG] 20 Dec[removed]Yunlong He∗ Add to Reading List Source URL: arxiv.org Language: English - Date: 2013-12-22 20:35:04 Cybernetics Learning Machine learning Sparse coding Matching pursuit Applied mathematics Artificial intelligence Caltech 101 Statistics Multivariate statistics Signal processing
276	Convolutional Neural Networks Applied to House Numbers Digit Classification Pierre Sermanet, Soumith Chintala and Yann LeCun The Courant Institute of Mathematical Sciences - New York University {sermanet,soumith,yann}@cs Add to Reading List Source URL: yann.lecun.com Language: English - Date: 2012-08-08 15:24:44 Learning Supervised learning Yann LeCun Statistical classification Neural network Pattern recognition Linear classifier Unsupervised learning Machine learning Artificial intelligence Statistics
277	Learning Invariant Feature Hierarchies Yann LeCun Courant Institute, New York University Abstract. Fast visual recognition in the mammalian cortex seems to be a hierarchical process by which the representation of the vis Add to Reading List Source URL: yann.lecun.com Language: English - Date: 2012-10-12 04:14:26 Learning Yann LeCun Unsupervised learning Neural network Supervised learning Pattern recognition Neocognitron Sparse coding Machine learning Artificial intelligence Science
278	Emergence of Object-Selective Features in Unsupervised Feature Learning Adam Coates, Andrej Karpathy, Andrew Y. Ng Computer Science Department Stanford University Add to Reading List Source URL: cs.stanford.edu Language: English - Date: 2012-11-23 20:28:37 Computational statistics Computer vision Pattern recognition Object recognition Supervised learning Invariant Cluster analysis Unsupervised learning K-means clustering Statistics Machine learning Artificial intelligence
279	Subspace Clustering, Ensemble Clustering, Alternative Clustering, Multiview Clustering: What Can We Learn From Each Other? Hans-Peter Kriegel Arthur Zimek Add to Reading List Source URL: www.dbs.ifi.lmu.de Language: English - Date: 2010-08-02 08:28:50 Machine learning Consensus clustering Multivariate statistics Constrained clustering Data mining Correlation clustering Unsupervised learning Hans-Peter Kriegel Clustering high-dimensional data Statistics Cluster analysis Data analysis
280	Unsupervised and Transfer Learning Challenge Isabelle Guyon, Gideon Dror, Vincent Lemaire, Graham Taylor, David W. Aha Abstract— We organized a data mining challenge in “unsupervised and transfer learning” (the UTL Add to Reading List Source URL: www.nrl.navy.mil Language: English - Date: 2013-08-26 10:18:54 Learning Unsupervised learning Supervised learning Pattern recognition Random forest Linear classifier Statistical classification Cross-validation Data mining Machine learning Statistics Artificial intelligence

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