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Learning theory
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#	Item
81	LETTER Communicated by Friedrich Sommer A Sparse Generative Model of V1 Simple Cells with Intrinsic Plasticity Add to Reading List Source URL: fias.uni-frankfurt.de Language: English - Date: 2008-09-09 03:37:37 Nervous system Computational neuroscience Neuroscience Computational statistics Artificial neural network Mathematical psychology Receptive field Biological neuron model Hebbian theory Visual cortex Feature learning Visual tilt effects
82	Quantifying Generalization in Linearly Weighted Neural Networks (Short title: Quantifying Generalization) Martin Anthony1 and Sean B. Holden2 Abstract Add to Reading List Source URL: www.maths.lse.ac.uk Language: English - Date: 2000-04-03 14:52:08 Machine learning Learning Artificial intelligence Computational learning theory Computational statistics Statistical classification Computational neuroscience VC dimension Artificial neural network Probably approximately correct learning Probability distribution Uniform convergence
83	C:/Users/Utente/Desktop/PhD/CAL_tex/ISMIR2010_DTM/ISMIR2010_DTM.dvi Add to Reading List Source URL: eceweb.ucsd.edu Language: English - Date: 2015-07-31 19:02:39 Statistics Estimation theory Physics Mechanical engineering Cluster analysis Machine learning Mixture model Expectationmaximization algorithm Maximum likelihood estimation Work
84	Simultaneous Learning of Motion and Sensor Model Parameters for Mobile Robots Teddy N. Yap, Jr. and Christian R. Shelton Department of Computer Science and Engineering University of California, Riverside, CA 92521, USA { Add to Reading List Source URL: rlair.cs.ucr.edu Language: English - Date: 2011-01-19 19:25:14 Robot control Statistics Robot navigation Estimation theory Engineering Artificial intelligence Linear filters Simultaneous localization and mapping Monte Carlo methods Kalman filter Robot calibration Sensor
85	Network Analysis and Modeling, CSCI 5352 LectureProf. Aaron Clauset Add to Reading List Source URL: tuvalu.santafe.edu Language: English - Date: 2013-11-23 11:09:19 Graph theory Mathematics Discrete mathematics Random graphs Networks Network theory Machine learning Stochastic block model Vertex Graph Bipartite graph Connectivity
86	Fast Process Variation Analysis in Nano-Scaled Technologies Using Column-Wise Sparse Parameter Selection Hassan Ghasemzadeh Mohammadi∗ , Pierre-Emmanuel Gaillardon∗ , Majid Yazdani† , Giovanni De Micheli∗ Integra Add to Reading List Source URL: majid.yazdani.me Language: English - Date: 2015-08-19 06:57:43 Software Estimation theory Statistics Machine learning Application software Actuarial science Regression analysis SPICE Pattern recognition Linear regression Structural estimation Semiconductor device modeling
87	Better Classifier Chains for Multi-label Classification Jesse Read, Fernando P´erez Cruz Department of Signal Theory and Communications Universidad Carlos III Madrid, Spain Add to Reading List Source URL: users.ics.aalto.fi Language: English - Date: 2011-07-21 06:05:16 Machine learning Classifier chains Statistical classification Multi-label classification Classifier Supervised learning Cognition Information
88	Learning Communities for Graduate Students: Supporting Scholarly Teaching Katie Kearns, PhD Stages of teaching development for graduate students Add to Reading List Source URL: www.iub.edu Language: English - Date: 2012-01-03 10:04:50 Education Euthenics Knowledge sharing Educational psychology Teaching Curricula Philosophy of education Pedagogy Student-centred learning SOTL Learning theory Educational assessment
89	CS364A: Algorithmic Game Theory Lecture #20: Mixed Nash Equilibria and PPAD-Completeness∗ Tim Roughgarden† December 4, 2013 Today we continue our study of the limitations of learning dynamics and polynomial-time Add to Reading List Source URL: theory.stanford.edu Language: English - Date: 2014-01-06 16:13:01 Computational complexity theory Theory of computation Complexity classes NP Clique problem Linear programming P Algorithm Time complexity Optimization problem Book:Graph Theory P versus NP problem
90	Evaluating Problem Difficulty Rankings Using Sparse Student Response Data Ari BADER-NATAL 1 , Jordan POLLACK DEMO Lab, Brandeis University Abstract. Problem difficulty estimates play important roles in a wide variety of Add to Reading List Source URL: aribadernatal.com Language: English - Date: 2016-06-29 00:36:46 Statistics Statistical models Psychometrics Machine learning Educational psychology Estimation theory Item response theory Rasch model Cross-validation Regression analysis SAT

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