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502,953
Lattice Basis Reduction: Improved Practical Algorithms and Solving Subset Sum Problems.
 Math. Programming
, 1993
"... We report on improved practical algorithms for lattice basis reduction. We propose a practical floating point version of the L3algorithm of Lenstra, Lenstra, Lov'asz (1982). We present a variant of the L3 algorithm with "deep insertions" and a practical algorithm for block KorkinZ ..."
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Cited by 327 (6 self)
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Zolotarev reduction, a concept introduced by Schnorr (1987). Empirical tests show that the strongest of these algorithms solves almost all subset sum problems with up to 66 random weights of arbitrary bit length within at most a few hours on a UNISYS 6000/70 or within a couple of minutes on a SPARC 1+ computer.
Solving Subset Sum Problems by Timefree Spiking Neural P Systems
"... Abstract: In membrane computing, spiking neural P systems (shortly called SN P systems) are a group of neurallike computing models inspired from the way spiking neurons communication in form of spikes. In previous works, SN P systems working in a nondeterministic manner have been used to solve nume ..."
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Cited by 1 (0 self)
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. To investigate the computational efficiency of timefree SN P systems, we solve Subset Sum problem by a family of uniform timefree SN P systems.
Solving Subset Sum Problems of Density close to 1 by ”randomized ” BKZreduction
, 2012
"... Abstract. Subset sum or Knapsack problems of dimension n are known to be hardest for knapsacks of density close to 1. These problems are NPhard for arbitrary n. One can solve such problems either by lattice basis reduction or by optimized birthday algorithms. Recently Becker, Coron, Joux [BCJ10] pr ..."
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Abstract. Subset sum or Knapsack problems of dimension n are known to be hardest for knapsacks of density close to 1. These problems are NPhard for arbitrary n. One can solve such problems either by lattice basis reduction or by optimized birthday algorithms. Recently Becker, Coron, Joux [BCJ10
Solving SubsetSum Problem by using Genetic Algorithm Approach
, 2012
"... Genetic Algorithms are evolutionary algorithms used to solve nondeterministic polynomial time problems generally known as npproblems. The search space of the problems are too large to be solve by conventional approaches. The basic idea of Genetic approach is analogous to biological evolution where ..."
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Genetic Algorithms are evolutionary algorithms used to solve nondeterministic polynomial time problems generally known as npproblems. The search space of the problems are too large to be solve by conventional approaches. The basic idea of Genetic approach is analogous to biological evolution
The Hungarian method for the assignment problem
 Naval Res. Logist. Quart
, 1955
"... Assuming that numerical scores are available for the performance of each of n persons on each of n jobs, the "assignment problem" is the quest for an assignment of persons to jobs so that the sum of the n scores so obtained is as large as possible. It is shown that ideas latent in the work ..."
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Cited by 1259 (0 self)
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Assuming that numerical scores are available for the performance of each of n persons on each of n jobs, the "assignment problem" is the quest for an assignment of persons to jobs so that the sum of the n scores so obtained is as large as possible. It is shown that ideas latent
SIGNAL RECOVERY BY PROXIMAL FORWARDBACKWARD SPLITTING
 MULTISCALE MODEL. SIMUL. TO APPEAR
"... We show that various inverse problems in signal recovery can be formulated as the generic problem of minimizing the sum of two convex functions with certain regularity properties. This formulation makes it possible to derive existence, uniqueness, characterization, and stability results in a unifi ..."
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Cited by 509 (24 self)
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We show that various inverse problems in signal recovery can be formulated as the generic problem of minimizing the sum of two convex functions with certain regularity properties. This formulation makes it possible to derive existence, uniqueness, characterization, and stability results in a
Shape Matching and Object Recognition Using Shape Contexts
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2001
"... We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by (1) solv ing for correspondences between points on the two shapes, (2) using the correspondences to estimate an aligning transform ..."
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Cited by 1809 (21 self)
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transform. In order to solve the correspondence problem, we attach a descriptor, the shape context, to each point. The shape context at a reference point captures the distribution of the remaining points relative to it, thus offering a globally discriminative characterization. Corresponding points on two
Hierarchically Classifying Documents Using Very Few Words
, 1997
"... The proliferation of topic hierarchies for text documents has resulted in a need for tools that automatically classify new documents within such hierarchies. Existing classification schemes which ignore the hierarchical structure and treat the topics as separate classes are often inadequate in text ..."
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Cited by 521 (8 self)
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tree. As we show, each of these smaller problems can be solved accurately by focusing only on a very small set of features, those relevant to the task at hand. This set of relevant features varies widely throughout the hierarchy, so that, while the overall relevant feature set may be large, each
Estimating the Support of a HighDimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
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Cited by 783 (29 self)
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propose a method to approach this problem by trying to estimate a function f which is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length
Integrating classification and association rule mining
 In Proc of KDD
, 1998
"... Classification rule mining aims to discover a small set of rules in the database that forms an accurate classifier. Association rule mining finds all the rules existing in the database that satisfy some minimum support and minimum confidence constraints. For association rule mining, the target of di ..."
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Cited by 578 (21 self)
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of discovery is not predetermined, while for classification rule mining there is one and only one predetermined target. In this paper, we propose to integrate these two mining techniques. The integration is done by focusing on mining a special subset of association rules, called class association rules (CARs
Results 1  10
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502,953