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Weierstrass and Approximation Theory

by Allan Pinkus
"... We discuss and examine Weierstrass' main contributions to approximation theory. ..."
Abstract - Cited by 189 (7 self) - Add to MetaCart
We discuss and examine Weierstrass' main contributions to approximation theory.

Approximation Theory of Output Statistics

by Te Sun Han, Sergio Verdg - IEEE Trans. Inform. Theory , 1993
"... Abstract-Given a channel and an input process, the minimum randomness of those input processes whose output statistics approximate the original output statistics with arbitrary accuracy is studied. The notion of resolvability of a channel, defined as the number of random bits required per channel us ..."
Abstract - Cited by 171 (11 self) - Add to MetaCart
Abstract-Given a channel and an input process, the minimum randomness of those input processes whose output statistics approximate the original output statistics with arbitrary accuracy is studied. The notion of resolvability of a channel, defined as the number of random bits required per channel

Approximate objects and approximate theories

by John Mccarthy - KR2000: Principles of Knowledge Representation and Reasoning,Proceedings of the Seventh International conference , 2000
"... We propose to extend the ontology of logical AI to include approximate objects, approximate predicates and approximate theories. Besides the ontology we treat the relations among different approximate theories of the same phenomena. Approximate predicates can’t have complete if-and-only-if definitio ..."
Abstract - Cited by 10 (2 self) - Add to MetaCart
We propose to extend the ontology of logical AI to include approximate objects, approximate predicates and approximate theories. Besides the ontology we treat the relations among different approximate theories of the same phenomena. Approximate predicates can’t have complete if

Determining the Number of Factors in Approximate Factor Models

by Jushan Bai, Serena Ng , 2000
"... In this paper we develop some statistical theory for factor models of large dimensions. The focus is the determination of the number of factors, which is an unresolved issue in the rapidly growing literature on multifactor models. We propose a panel Cp criterion and show that the number of factors c ..."
Abstract - Cited by 561 (30 self) - Add to MetaCart
In this paper we develop some statistical theory for factor models of large dimensions. The focus is the determination of the number of factors, which is an unresolved issue in the rapidly growing literature on multifactor models. We propose a panel Cp criterion and show that the number of factors

Greed is Good: Algorithmic Results for Sparse Approximation

by Joel A. Tropp , 2004
"... This article presents new results on using a greedy algorithm, orthogonal matching pursuit (OMP), to solve the sparse approximation problem over redundant dictionaries. It provides a sufficient condition under which both OMP and Donoho’s basis pursuit (BP) paradigm can recover the optimal representa ..."
Abstract - Cited by 916 (9 self) - Add to MetaCart
This article presents new results on using a greedy algorithm, orthogonal matching pursuit (OMP), to solve the sparse approximation problem over redundant dictionaries. It provides a sufficient condition under which both OMP and Donoho’s basis pursuit (BP) paradigm can recover the optimal

Constructing Free Energy Approximations and Generalized Belief Propagation Algorithms

by Jonathan S. Yedidia, William T. Freeman, Yair Weiss - IEEE Transactions on Information Theory , 2005
"... Important inference problems in statistical physics, computer vision, error-correcting coding theory, and artificial intelligence can all be reformulated as the computation of marginal probabilities on factor graphs. The belief propagation (BP) algorithm is an efficient way to solve these problems t ..."
Abstract - Cited by 585 (13 self) - Add to MetaCart
Important inference problems in statistical physics, computer vision, error-correcting coding theory, and artificial intelligence can all be reformulated as the computation of marginal probabilities on factor graphs. The belief propagation (BP) algorithm is an efficient way to solve these problems

Large N field theories, string theory and gravity

by Ofer Aharony, Steven S. Gubser, Juan Maldacena, Hirosi Ooguri, Yaron Oz , 2001
"... We review the holographic correspondence between field theories and string/M theory, focusing on the relation between compactifications of string/M theory on Anti-de Sitter spaces and conformal field theories. We review the background for this correspondence and discuss its motivations and the evide ..."
Abstract - Cited by 1443 (45 self) - Add to MetaCart
and the evidence for its correctness. We describe the main results that have been derived from the correspondence in the regime that the field theory is approximated by classical or semiclassical gravity. We focus on the case of the N = 4 supersymmetric gauge theory in four dimensions, but we discuss also field

Stochastic Perturbation Theory

by G. W. Stewart , 1988
"... . In this paper classical matrix perturbation theory is approached from a probabilistic point of view. The perturbed quantity is approximated by a first-order perturbation expansion, in which the perturbation is assumed to be random. This permits the computation of statistics estimating the variatio ..."
Abstract - Cited by 907 (36 self) - Add to MetaCart
. In this paper classical matrix perturbation theory is approached from a probabilistic point of view. The perturbed quantity is approximated by a first-order perturbation expansion, in which the perturbation is assumed to be random. This permits the computation of statistics estimating

Density in Approximation Theory

by Allan Pinkus , 2005
"... Approximation theory is concerned with the ability to approximate functions by simpler and more easily calculated functions. The first question we ask in approximation theory concerns the possibility of approximation. Is the given family of functions from which we plan to approximate dense in the ..."
Abstract - Cited by 6 (0 self) - Add to MetaCart
Approximation theory is concerned with the ability to approximate functions by simpler and more easily calculated functions. The first question we ask in approximation theory concerns the possibility of approximation. Is the given family of functions from which we plan to approximate dense

Property Testing and its connection to Learning and Approximation

by Oded Goldreich, Shafi Goldwasser, Dana Ron
"... We study the question of determining whether an unknown function has a particular property or is ffl-far from any function with that property. A property testing algorithm is given a sample of the value of the function on instances drawn according to some distribution, and possibly may query the fun ..."
Abstract - Cited by 475 (67 self) - Add to MetaCart
the function on instances of its choice. First, we establish some connections between property testing and problems in learning theory. Next, we focus on testing graph properties, and devise algorithms to test whether a graph has properties such as being k-colorable or having a ae-clique (clique of density ae
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