Results 1  10
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13,949
Greed is Good: Algorithmic Results for Sparse Approximation
, 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 ..."
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Cited by 916 (9 self)
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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
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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in a more gen eral setting? We compare the marginals com puted using loopy propagation to the exact ones in four Bayesian network architectures, including two realworld networks: ALARM and QMR. We find that the loopy beliefs of ten converge and when they do, they give a good approximation
SQUARE ROOTS WITH MANY GOOD APPROXIMANTS
 INTEGERS: ELECTRONIC JOURNAL OF COMBINATORIAL NUMBER THEORY 5(3) (2005), #A06
, 2005
"... Let d be a positive integer that is not a perfect square. It was proved by Mikusiński in 1954 that if the period s(d) of the continued fraction expansion of d satisfies s(d) ≤ 2, then all Newton’s approximants Rn = ..."
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Let d be a positive integer that is not a perfect square. It was proved by Mikusiński in 1954 that if the period s(d) of the continued fraction expansion of d satisfies s(d) ≤ 2, then all Newton’s approximants Rn =
Maximum Likelihood Phylogenetic Estimation from DNA Sequences with Variable Rates over Sites: Approximate Methods
 J. Mol. Evol
, 1994
"... Two approximate methods are proposed for maximum likelihood phylogenetic estimation, which allow variable rates of substitution across nucleotide sites. Three data sets with quite different characteristics were analyzed to examine empirically the performance of these methods. The first, called ..."
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Cited by 557 (29 self)
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the "discrete gamma model," uses several categories of rates to approximate the gamma distribution, with equal probability for each category. The mean of each category is used to represent all the rates falling in the category. The performance of this method is found to be quite good
Empirically Good Approximations for the Relative Neighbourhood Graph
, 2002
"... The Urquhart graph of a set of points in the plane is obtained by removing the longest edge from each triangle in the Delaunay triangulation. We show experimental evidence that the Urquhart graph is a good approximation for the relative neighbourhood graph in the sense that it contains few additiona ..."
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The Urquhart graph of a set of points in the plane is obtained by removing the longest edge from each triangle in the Delaunay triangulation. We show experimental evidence that the Urquhart graph is a good approximation for the relative neighbourhood graph in the sense that it contains few
Text Classification using String Kernels
"... We propose a novel approach for categorizing text documents based on the use of a special kernel. The kernel is an inner product in the feature space generated by all subsequences of length k. A subsequence is any ordered sequence of k characters occurring in the text though not necessarily contiguo ..."
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Cited by 495 (7 self)
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) show positive results on modestly sized datasets. The case of contiguous subsequences is also considered for comparison with the subsequences kernel with di erent decay factors. For larger documents and datasets the paper introduces an approximation technique that is shown to deliver good
Learning lowlevel vision
 International Journal of Computer Vision
, 2000
"... We show a learningbased method for lowlevel vision problems. We setup a Markov network of patches of the image and the underlying scene. A factorization approximation allows us to easily learn the parameters of the Markov network from synthetic examples of image/scene pairs, and to e ciently prop ..."
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Cited by 579 (30 self)
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propagate image information. Monte Carlo simulations justify this approximation. We apply this to the \superresolution " problem (estimating high frequency details from a lowresolution image), showing good results. For the motion estimation problem, we show resolution of the aperture problem
Policy gradient methods for reinforcement learning with function approximation.
 In NIPS,
, 1999
"... Abstract Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and determining a policy from it has so far proven theoretically intractable. In this paper we explore an alternative approach in which the policy is explicitly repres ..."
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Cited by 439 (20 self)
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is known to follow ∂ρ ∂θ in expected value Policy Gradient with Approximation Now consider the case in which Q π is approximated by a learned function approximator. If the approximation is sufficiently good, we might hope to use it in place of Q π in (2) and still point roughly in the direction
Smooth Refinable Functions Provide Good Approximation Orders
, 1995
"... We apply the general theory of approximation orders of shiftinvariant spaces of [BDR13] to the special case when the finitely many generators Φ ⊂ L2(IR d) of the underlying space S satisfy an Nscale relation (i.e., they form a “father wavelet ” set). We show that the approximation orders provided ..."
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Cited by 28 (9 self)
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We apply the general theory of approximation orders of shiftinvariant spaces of [BDR13] to the special case when the finitely many generators Φ ⊂ L2(IR d) of the underlying space S satisfy an Nscale relation (i.e., they form a “father wavelet ” set). We show that the approximation orders
Results 1  10
of
13,949