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108,517
Recursive Greedy Methods
, 2008
"... Greedy algorithms are often the first algorithm that one considers for various optimization problems, and,in particular, covering problems. The idea is very simple: try to build a solution incrementally by augmenting a partial solution. In each iteration, select the “best” augmentation according to ..."
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Greedy algorithms are often the first algorithm that one considers for various optimization problems, and,in particular, covering problems. The idea is very simple: try to build a solution incrementally by augmenting a partial solution. In each iteration, select the “best” augmentation according
On the impossibility of uniform sparse reconstruction using greedy methods
 Sampl. Theory Signal Image Process
"... It has previously shown that a trigonometric polynomial having at most M nonvanishing coefficients can be recovered from N = O(M log(D)) random samples by the greedy methods thresholding and orthogonal matching pursuit with high probability. In this note we show that these results cannot be made uni ..."
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Cited by 33 (8 self)
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It has previously shown that a trigonometric polynomial having at most M nonvanishing coefficients can be recovered from N = O(M log(D)) random samples by the greedy methods thresholding and orthogonal matching pursuit with high probability. In this note we show that these results cannot be made
A greedy algorithm for aligning DNA sequences
 J. COMPUT. BIOL
, 2000
"... For aligning DNA sequences that differ only by sequencing errors, or by equivalent errors from other sources, a greedy algorithm can be much faster than traditional dynamic programming approaches and yet produce an alignment that is guaranteed to be theoretically optimal. We introduce a new greedy a ..."
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Cited by 576 (16 self)
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For aligning DNA sequences that differ only by sequencing errors, or by equivalent errors from other sources, a greedy algorithm can be much faster than traditional dynamic programming approaches and yet produce an alignment that is guaranteed to be theoretically optimal. We introduce a new greedy
Greedy Function Approximation: A Gradient Boosting Machine
 Annals of Statistics
, 2000
"... Function approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest{descent minimization. A general gradient{descent \boosting" paradigm is developed for additi ..."
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Cited by 951 (12 self)
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data. Connections between this approach and the boosting methods of Freund and Shapire 1996, and Frie...
Greedy Methods in Plume Detection, Localization and Tracking
 Advances in Greedy Algorithms , edited by W. Bednorz, InTech
, 2008
"... Greedy method, as an efficient computing tool, can be applied to various combinatorial or nonlinear optimization problems where finding the global optimum is difficult, if not computationally infeasible. A greedy algorithm has the nature of making the locally optimal choice at each stage and then so ..."
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Cited by 2 (0 self)
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Greedy method, as an efficient computing tool, can be applied to various combinatorial or nonlinear optimization problems where finding the global optimum is difficult, if not computationally infeasible. A greedy algorithm has the nature of making the locally optimal choice at each stage
A Framework of Greedy Methods for Constructing Interaction Test Suites
 Intl. Conf. on Software Engineering (ICSE’05
, 2005
"... Greedy algorithms for the construction of software interaction test suites are studied. A framework is developed to evaluate a large class of greedy methods that build suites one test at a time. Within this framework are many instantiations of greedy methods generalizing those in the literature. Gre ..."
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Cited by 33 (8 self)
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Greedy algorithms for the construction of software interaction test suites are studied. A framework is developed to evaluate a large class of greedy methods that build suites one test at a time. Within this framework are many instantiations of greedy methods generalizing those in the literature
On learning discrete graphical models using greedy methods
 In Neural Information Processing Systems (NIPS) (currently under review
, 2011
"... In this paper, we address the problem of learning the structure of a pairwise graphical model from samples in a highdimensional setting. Our first main result studies the sparsistency, or consistency in sparsity pattern recovery, properties of a forwardbackward greedy algorithm as applied to gener ..."
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Cited by 27 (5 self)
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In this paper, we address the problem of learning the structure of a pairwise graphical model from samples in a highdimensional setting. Our first main result studies the sparsistency, or consistency in sparsity pattern recovery, properties of a forwardbackward greedy algorithm as applied
Greedy method for inferring tandem duplication history
"... Motivation: Genome analysis suggests that tandem duplication is an important mode of evolutionary novelty by permitting one copy of each gene to drift and potentially to acquire a new function. With more and more genomic sequences available, reconstructing duplication history has received extensive ..."
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Cited by 7 (0 self)
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attention recently. Results: An efficient method is presented for inferring the duplication history of tandemly repeated sequences based on the model proposed by Fitch (1977). We validate the method by using simulation results and real data sets of mucin genes, ZNF genes, and olfactory receptors genes
A New Method for Solving Hard Satisfiability Problems
 AAAI
, 1992
"... We introduce a greedy local search procedure called GSAT for solving propositional satisfiability problems. Our experiments show that this procedure can be used to solve hard, randomly generated problems that are an order of magnitude larger than those that can be handled by more traditional approac ..."
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Cited by 734 (21 self)
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We introduce a greedy local search procedure called GSAT for solving propositional satisfiability problems. Our experiments show that this procedure can be used to solve hard, randomly generated problems that are an order of magnitude larger than those that can be handled by more traditional
Ensemble Methods in Machine Learning
 MULTIPLE CLASSIFIER SYSTEMS, LBCS1857
, 2000
"... Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include errorcorrecting output coding, Bagging, and boostin ..."
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Cited by 607 (3 self)
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Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include errorcorrecting output coding, Bagging
Results 1  10
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108,517