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Supervised learning
Results: 1557

#	Item
531	Unsupervised and Semi-supervised Multi-class Support Vector Machines Linli Xu∗ Dale Schuurmans School of Computer Science Add to Reading List Source URL: webdocs.cs.ualberta.ca Language: English - Date: 2005-06-06 21:10:08 Machine learning Operations research Convex analysis Statistical classification Support vector machine Semidefinite programming Convex optimization Quadratic programming Eigenvalues and eigenvectors Mathematical optimization Statistics Mathematics
532	Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling Feng Jiao University of Waterloo Abstract We present a new semi-supervised training Add to Reading List Source URL: webdocs.cs.ualberta.ca Language: English - Date: 2006-12-28 15:24:48 Statistical theory Estimation theory Conditional random field Theoretical computer science Supervised learning Kullback–Leibler divergence Semi-supervised learning Expectation–maximization algorithm Conditional entropy Statistics Machine learning Information theory
533	Semi-Supervised Structuring of Complex Data Marian-Andrei RIZOIU ERIC Laboratory, University Lumière, Lyon, France August 5th, 2013 Add to Reading List Source URL: www.rizoiu.eu Language: English - Date: 2013-10-04 10:30:17 Cybernetics Learning Machine learning
534	Application of machine learning in industrial radiographic testing Add to Reading List Source URL: www.data-mining-tutorial.de Language: English - Date: 2009-10-09 07:20:40 Computational statistics Decision trees Computational neuroscience Pruning Backpropagation Supervised learning Perceptron Decision tree learning Multilayer perceptron Statistics Machine learning Neural networks
535	Optimal Reverse Prediction A Unified Perspective on Supervised, Unsupervised and Semi-supervised Learning Linli Xu Martha White Add to Reading List Source URL: webdocs.cs.ualberta.ca Language: English - Date: 2009-04-23 13:42:37 Mathematics Regression analysis Numerical linear algebra Linear algebra Estimation theory Semi-supervised learning Supervised learning Pattern recognition Unsupervised learning Statistics Machine learning Algebra
536	1 Real-time Discriminative Background Subtraction Li Cheng, Member, Minglun Gong Member, Dale Schuurmans, Terry Caelli Fellow Add to Reading List Source URL: webdocs.cs.ualberta.ca Language: English - Date: 2010-12-15 01:15:33 Computational statistics Segmentation Support vector machine Supervised learning Machine learning Stochastic gradient descent Statistics Image processing Learning
537	Artificial Neural Networks Examination, March 2002 Instructions There are SIXTY questions (worth up to 60 marks). The exam mark (maximum 60) will be added to the mark obtained in the laborations (maximum 5). The total pa Add to Reading List Source URL: aass.oru.se Language: English - Date: 2004-06-07 09:38:12 Statistics Perceptron Artificial neural network Multilayer perceptron Activation function Backpropagation ADALINE Supervised learning Competitive learning Neural networks Machine learning Cybernetics
538	Object recognition learning algorithms that are easily trained, accurate, and capable of relating decisions to the feature vec Add to Reading List Source URL: eecs.vanderbilt.edu Language: English - Date: 2005-07-27 15:28:48 Artificial intelligence Vision Mathematics Feature vector Euclidean vector Matrix Feature Supervised learning Object recognition Computer vision Machine learning Image processing
539	Artificial Neural Networks Examination, June/August 2003 Instructions There are SIXTY questions (worth up to 60 marks). The exam mark (maximum 60) will be added to the mark obtained in the laborations (maximum 5). The to Add to Reading List Source URL: aass.oru.se Language: English - Date: 2004-06-07 09:38:12 Statistics Perceptron Artificial neural network Multilayer perceptron Generalization error Backpropagation Supervised learning Statistical classification Pattern recognition Neural networks Machine learning Cybernetics
540	Artificial Neural Networks Examination, March 2005 Instructions There are SIXTY questions. (The pass mark is 30 out of 60.) For each question, please select a maximum of ONE of the given answers (either A, B, C, D or E). Add to Reading List Source URL: aass.oru.se Language: English - Date: 2005-03-21 10:47:39 Learning Artificial neuron Artificial neural network Perceptron Generalization error Statistical classification Supervised learning Multilayer perceptron Activation function Neural networks Cybernetics Artificial intelligence

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