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Linear function
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261	Chapter 3 Interpolation Interpolation is the process of deﬁning a function that takes on speciﬁed values at speciﬁed points. This chapter concentrates on two closely related interpolants: the Add to Reading List Source URL: www.mathworks.com Language: English - Date: 2013-09-25 13:50:03 Mathematical analysis Polynomial interpolation Linear interpolation Lagrange polynomial Spline Hermite interpolation Polynomial Vandermonde matrix Piecewise Interpolation Numerical analysis Mathematics
262	Image Texture Classification Based on Finite Gaussian Mixture Models Yiming Wu, Kap Luk Chan, Yong Huang School of Electrical and Electronic Engineering Nanyang Technological University Nanyang Avenue, Singapore, 639798 Add to Reading List Source URL: www.macs.hw.ac.uk Language: English - Date: 2003-09-11 06:46:48 Probability Gaussian function Image processing Linear filters Wavelet Mixture model Scale space Steerable pyramid Normal distribution Statistics Computer vision Probability and statistics
263	Theories of coalitional rationality∗ Attila Ambrus† Abstract This paper generalizes the concept of best response to coalitions of players and oﬀers epistemic definitions of coalitional rationalizability in normal f Add to Reading List Source URL: public.econ.duke.edu Language: English - Date: 2012-08-28 11:36:22 Science Cooperative game Sheaf Function Entailment Interpretation Nash equilibrium Solution concept Linear temporal logic Logic Game theory Mathematics
264	Submitted to the Statistical Science A unified framework for high-dimensional analysis of M -estimators with decomposable regularizers Add to Reading List Source URL: www.eecs.berkeley.edu Language: English - Date: 2012-06-03 10:52:44 Econometrics Statistical inference Regression analysis Maximum likelihood Parametric model Linear regression Consistent estimator Matrix norm Loss function Statistics Estimation theory Statistical theory
265	Software Tools for Technology Transfer manuscript No. (will be inserted by the editor) Functional Synthesis for Linear Arithmetic and Sets Viktor Kuncak, Mika¨ el Mayer, Ruzica Piskac, Philippe Suter? Add to Reading List Source URL: lara.epfl.ch Language: English - Date: 2011-11-29 17:36:57 Model theory Logic in computer science Constraint programming Predicate logic Semantics Satisfiability Modulo Theories Quantifier elimination First-order logic Function Logic Mathematical logic Mathematics
266	The R package cccp: Design for solving cone constrained convex programs Bernhard Pfaff Invesco Asset Management GmbH Add to Reading List Source URL: www.pfaffikus.de Language: English - Date: 2015-05-25 17:53:46 Convex analysis Convex optimization Linear programming Semidefinite programming Convex function Interior point method CPLEX Mathematical optimization Mathematical analysis Operations research
267	Domain Adaptation under Target and Conditional Shift Kun Zhang Bernhard Sch¨ olkopf Krikamol Muandet Add to Reading List Source URL: people.tuebingen.mpg.de Language: English - Date: 2013-05-08 21:10:46 Estimation theory Parametric statistics Linear regression Kullback–Leibler divergence Gaussian function Function Nonlinear regression Degrees of freedom Statistics Regression analysis Econometrics
268	Probabilistic Modeling of Human Movements for Intention Inference Zhikun Wang1,2 , Marc Peter Deisenroth2 , Heni Ben Amor2 , David Vogt3 , Bernhard Schölkopf1 , Jan Peters1,2 1 MPI for Intelligent Systems, Spemannstr. 3 Add to Reading List Source URL: www.is.tuebingen.mpg.de Language: English - Date: 2012-06-11 09:24:01 Linear filters Markov models Bayesian statistics Kalman filter Expectation–maximization algorithm Hidden Markov model Gaussian process Gaussian function Normal distribution Statistics Estimation theory Robot control
269	Supplemental Material : A Unified Framework for Multi-Target Tracking and Collective Activity Recognition Wongun Choi and Silvio Savarese Electrical and Computer Engineering, University of Michigan, Ann Arbor, USA {wgcho Add to Reading List Source URL: cvgl.stanford.edu Language: English - Date: 2014-04-08 01:52:59 Flow network Network flow Function Linear temporal logic Mathematics Operations research Functions and mappings
270	Learning Submodular Losses with the Lov´ asz Hinge Jiaqian Yu, Matthew Blaschko To cite this version: Jiaqian Yu, Matthew Blaschko. Learning Submodular Losses with the Lov´asz Hinge. International Conference on Machine Add to Reading List Source URL: hal.inria.fr Language: English Machine learning Submodular set function Order theory Convex analysis Supermodular function Hinge loss Empirical risk minimization Linear programming Vector space Mathematics Mathematical optimization Algebra

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