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Computational learning theory
Results: 425

#	Item
181	2D1431 Machine Learning Exam 2002-02-14 8:00-13:00 L21-22 Frank Homann e-mail: homann@nada.kth.se December 20, 2002 Add to Reading List Source URL: www.nada.kth.se Language: English Cybernetics Artificial intelligence Statistical inference Learning Computational learning theory Inductive bias Probably approximately correct learning Artificial neuron Statistical hypothesis testing Statistics Machine learning Science
182	Algorithms for Nearest Neighbors Background and Two Challenges Yury Lifshits Steklov Institute of Mathematics at St.Petersburg http://logic.pdmi.ras.ru/~yura Add to Reading List Source URL: yury.name Language: English - Date: 2007-12-08 06:42:12 Search algorithms Computational statistics K-nearest neighbor algorithm Plagiarism detection Coding theory Pattern recognition Statistical classification Nearest neighbor search Machine learning Artificial intelligence Information science
183	Three quantum learning algorithms Ashley Montanaro Talk based on joint work with Andris Ambainis and ongoing joint work with Scott Aaronson, David Chen, Daniel Gottesman and Vincent Liew. 11 March 2013 Add to Reading List Source URL: www.damtp.cam.ac.uk Language: English - Date: 2013-03-11 13:38:24 Quantum algorithm Quantum information science Pierian Spring FO Decision tree model Theoretical computer science Applied mathematics Computational complexity theory
184	Computational Creativity Theory: The FACE and IDEA Descriptive Models Add to Reading List Source URL: www.thepaintingfool.com Language: English - Date: 2013-10-24 13:18:46 Cognition Creativity Positive psychology Computational creativity Education Mind Psychology Concept learning Generative Educational psychology Problem solving Artificial intelligence
185	Sample complexity of stochastic least squares system identi cation Erik Weyer1 Department of Chemical Engineering The University of Queensland Brisbane QLD 4072, Australia Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2005-03-22 04:24:05 Statistical classification VC dimension Statistics Computational learning theory Dimension
186	On Efficient Agnostic Learning of Linear Combinations of Basis Functions Wee Sun Lee Dept. of Systems Engineering, RSISE, Aust. National University, Canberra, ACT 0200, Australia. Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2005-03-22 04:23:34 Probably approximately correct learning Inner product space Polynomial Function Computational complexity theory Machine learning Supervised learning Empirical risk minimization Mathematics Mathematical analysis Computational learning theory
187	SOME RESULTS IN STATISTICAL LEARNING THEORY WITH RELEVANCE TO NONLINEAR SYSTEM IDENTIFICATION Robert C. Williamson Department of Engineering, Australian National University, Canberra, Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2005-03-27 00:08:49 Dimension Statistical classification VC dimension Glivenko–Cantelli theorem Function Regression analysis Linear regression Statistics Econometrics Computational learning theory
188	Phase Transitions: A ew Paradigm for Evaluating Complexity in Learning and other Complex Systems Lorenza Saitta Università del Piemonte Orientale, Dipartimento di Informatica Viale Teresa Michel 11, 15121 Alessandria, Add to Reading List Source URL: www.iiconference.org Language: English - Date: 2013-03-08 07:30:15 Computational complexity theory Complexity Algorithm Boolean satisfiability problem Constraint satisfaction problem Theoretical computer science Applied mathematics Mathematics
189	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
190	Effectiveness of Neural Network Learning Rules Generated by a Biophysical Model of Synaptic Plasticity David J. Jilk, Daniel M. Cer, and Randall C. O’Reilly Department of Psychology University of Colorado, Boulder 345 Add to Reading List Source URL: dmcer.net Language: English - Date: 2011-07-25 01:42:34 Neural networks Neurophysiology Computational neuroscience Cell signaling Hebbian theory Synaptic weight Synaptic plasticity Spike-timing-dependent plasticity Chemical synapse Biology Neuroscience Nervous system

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