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Multilayer perceptron
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51	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
52	Artificial Neural Networks Examination, March 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 total pa Add to Reading List Source URL: aass.oru.se Language: English - Date: 2004-06-07 09:38:12 Neuroscience Perceptron Artificial neural network Multilayer perceptron Backpropagation Pattern recognition Statistical classification Winner-take-all Activation function Neural networks Cybernetics Science
53	Artificial Neural Networks Examination, March 2004 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 Artificial intelligence Feedforward neural network Perceptron Artificial neuron Artificial neural network Multilayer perceptron Backpropagation Generalization error ADALINE Neural networks Cybernetics Science
54	Neural Decision Forests for Semantic Image Labelling Samuel Rota Bul`o Fondazione Bruno Kessler Trento, Italy Peter Kontschieder Add to Reading List Source URL: www.dsi.unive.it Language: English - Date: 2014-04-15 04:23:50 Perceptron Multilayer perceptron Maximum likelihood Artificial neuron Random forest Activation function Decision tree learning Backpropagation Artificial neural network Neural networks Machine learning Statistics
55	Artificial Neural Networks Examination, June 2004 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 pas Add to Reading List Source URL: aass.oru.se Language: English - Date: 2004-09-08 10:30:53 Neuroscience Perceptron Feedforward neural network Artificial neural network Multilayer perceptron ADALINE Activation function Statistical classification Supervised learning Neural networks Cybernetics Science
56	Face Detection with Neural Networks Samuel Rota Bul` o Universit` a Ca’ Foscari Venezia DAIS Add to Reading List Source URL: www.dsi.unive.it Language: English - Date: 2012-10-15 07:26:11 Artificial intelligence Face detection Perceptron Multilayer perceptron Supervised learning Backpropagation Cellular neural network Artificial neural network Neural networks Machine learning Cybernetics
57	Articial Neural Networks Read Ch. 4] Recommended exercises 4.1, 4.2, 4.5, 4.9, 4.11] Threshold units Gradient descent Multilayer networks Add to Reading List Source URL: aass.oru.se Language: English - Date: 2005-03-31 12:57:42 Cybernetics Science Learning Perceptron Cognitive science Backpropagation Connectionism Artificial neuron Feedforward neural network Neural networks Machine learning Computational neuroscience
58	Forecasting the role of public expenditure in economic growth Using DEA-neural network approach Arshia Amiri a, b,, and Bruno Ventelou b, a b Add to Reading List Source URL: plagiarism.repec.org Language: English* - Date: 2012-05-25 11:20:58 Science Data envelopment analysis Artificial neural network Multilayer perceptron Artificial neuron Backpropagation Perceptron Forecasting Types of artificial neural networks Neural networks Cybernetics Statistics
59	M PRA Munich Personal RePEc Archive Forecasting the role of public expenditure in economic growth Using DEA-neural network approach Add to Reading List Source URL: plagiarism.repec.org Language: English - Date: 2011-10-09 05:37:38 Science Data envelopment analysis Artificial neural network Multilayer perceptron Backpropagation Artificial neuron Perceptron Forecasting Activation function Neural networks Cybernetics Statistics
60	Compete to Compute Rupesh Kumar Srivastava, Jonathan Masci, Sohrob Kazerounian, Faustino Gomez, Jürgen Schmidhuber IDSIA, USI-SUPSI Manno–Lugano, Switzerland Add to Reading List Source URL: people.idsia.ch Language: English - Date: 2014-07-04 07:33:06 Neuroscience Artificial neural network Feedforward neural network Backpropagation Autoencoder Biological neural network Winner-take-all Multilayer perceptron Perceptron Neural networks Cybernetics Science

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