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731
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
Maximizing the Spread of Influence Through a Social Network
 In KDD
, 2003
"... Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in gametheoretic settings, and the effects of ..."
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Cited by 990 (7 self)
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the first provable approximation guarantees for efficient algorithms. Using an analysis framework based on submodular functions, we show that a natural greedy strategy obtains a solution that is provably within 63 % of optimal for several classes of models; our framework suggests a general approach
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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into estimating a value function, with the actionselection policy represented implicitly as the "greedy" policy with respect to the estimated values (e.g., as the policy that selects in each state the action with highest estimated value). The valuefunction approach has worked well in many applications
A new greedy approach for facility location problems
"... We present a simple and natural greedy algorithm for the metric uncapacitated facility location problem achieving an approximation guarantee of 1.61 whereas the best previously known was 1.73. Furthermore, we will show that our algorithm has a property which allows us to apply the technique of Lagra ..."
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Cited by 145 (9 self)
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We present a simple and natural greedy algorithm for the metric uncapacitated facility location problem achieving an approximation guarantee of 1.61 whereas the best previously known was 1.73. Furthermore, we will show that our algorithm has a property which allows us to apply the technique
Efficient greedy learning of Gaussian mixture models
 Neural Computation
, 2003
"... This paper concerns the greedy learning of Gaussian mixtures. In the greedy approach, mixture components are inserted into the mixture one after the other. We propose a heuristic for searching for the optimal component to insert. In a randomized manner a set of candidate new components is generated. ..."
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Cited by 76 (7 self)
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and quadratic in the (final) number of mixture components. Due to its greedy nature the algorithm can be particularly useful when the optimal number of mixture components is unknown. Experimental results comparing the proposed algorithm to other methods on density estimation and texture segmentation
Induction of Selective Bayesian Classifiers
 CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE
, 1994
"... In this paper, we examine previous work on the naive Bayesian classifier and review its limitations, which include a sensitivity to correlated features. We respond to this problem by embedding the naive Bayesian induction scheme within an algorithm that carries out a greedy search through the space ..."
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Cited by 265 (7 self)
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In this paper, we examine previous work on the naive Bayesian classifier and review its limitations, which include a sensitivity to correlated features. We respond to this problem by embedding the naive Bayesian induction scheme within an algorithm that carries out a greedy search through the space
Greedy Algorithm:
"... A dominating set of a graph G = (V, E) is a subset S ⊆ V of the nodes such that for all nodes v ∈ V, either v ∈ S or a neighbor u of v is in S. There are many distributed applications where computing a small dominating set of the network graph is important. It is wellknown that computing a dominati ..."
Efficient Greedy Learning of Gaussian . . .
 NEURAL COMPUTATION
, 2003
"... This paper concerns the greedy learning of Gaussian mixtures. In the greedy approach, mixture components are inserted into the mixture one after the other. We propose a heuristic for searching for the optimal component to insert. In a randomized manner a set of candidate new components is generat ..."
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points and quadratic in the (final) number of mixture components. Due to its greedy nature the algorithm can be particularly useful when the optimal number of mixture components is unknown. Experimental results comparing the proposed algorithm to other methods on density estimation and texture
A NonIterative Greedy Algorithm for Multiframe Point Correspondence
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2003
"... This paper presents a framework for finding point correspondences in monocular image sequences over multiple frames. The general problem of multiframe point correspondence is NP Hard for three or more frames. A polynomial time algorithm for a restriction of this problem is presented and is used a ..."
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Cited by 85 (7 self)
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as the basis of the proposed greedy algorithm for the general problem. The greedy nature of the proposed algorithm allows it to be used in real time systems for tracking and surveillance etc. In addition, the proposed algorithm deals with the problems of occlusion, missed detections and false positives
The Tresholding Greedy Algorithm, Greedy Bases, and Duality
 CONSTR. APPROX
, 2001
"... Some new conditions that arise naturally in the study of the Thresholding Greedy Algorithm are introduced for bases of Banach spaces. We relate these conditions to best nterm approximation and we study their duality theory. In particular, we obtain a complete duality theory for greedy bases. ..."
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Cited by 19 (10 self)
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Some new conditions that arise naturally in the study of the Thresholding Greedy Algorithm are introduced for bases of Banach spaces. We relate these conditions to best nterm approximation and we study their duality theory. In particular, we obtain a complete duality theory for greedy bases.
Results 1  10
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731