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Results 1 - 10
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276,815
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
Good Error-Correcting Codes based on Very Sparse Matrices
, 1999
"... We study two families of error-correcting codes defined in terms of very sparse matrices. "MN" (MacKay--Neal) codes are recently invented, and "Gallager codes" were first investigated in 1962, but appear to have been largely forgotten, in spite of their excellent properties. The ..."
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Cited by 750 (23 self)
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. The decoding of both codes can be tackled with a practical sum-product algorithm. We prove that these codes are "very good," in that sequences of codes exist which, when optimally decoded, achieve information rates up to the Shannon limit. This result holds not only for the binary-symmetric channel
On Spectral Clustering: Analysis and an algorithm
- ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS
, 2001
"... Despite many empirical successes of spectral clustering methods -- algorithms that cluster points using eigenvectors of matrices derived from the distances between the points -- there are several unresolved issues. First, there is a wide variety of algorithms that use the eigenvectors in slightly ..."
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Cited by 1713 (13 self)
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the algorithm, and give conditions under which it can be expected to do well. We also show surprisingly good experimental results on a number of challenging clustering problems.
Cooperation and Punishment in Public Goods Experiments
- AMERICAN ECONOMIC REVIEW
, 2000
"... This paper provides evidence that free riders are heavily punished even if punishment is costly and does not provide any material benefits for the punisher. The more free riders negatively deviate from the group standard the more they are punished. As a consequence, the existence of an opportunity f ..."
Abstract
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Cited by 513 (38 self)
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of rationality and selfishness, there should be no cooperation at all. We also show that free riding causes strong negative emotions among cooperators. The intensity of these emotions is the stronger the more the free riders deviate from the group standard. Our results provide, therefore, support
Experimental Tests of the Endowment Effect and the Coase Theorem,”
- Journal of Political Economy,
, 1990
"... Contrary to theoretical expectations, measures of willingness to accept greatly exceed measures of willingness to pay. This paper reports several experiments that demonstrate that this "endowment effect" persists even in market settings with opportunities to learn. Consumption objects (e. ..."
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Cited by 677 (25 self)
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volume is observed, suggesting that transactions costs cannot explain the undertrading for consumption goods.
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
, 2000
"... In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in conver ..."
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Cited by 628 (41 self)
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, the experimental results indicate a hierarchy of the algorithms under consideration. Furthermore, the emerging effects are evidence that the suggested test functions provide sufficient complexity to compare multiobjective optimizers. Finally, elitism is shown to be an important factor for improving evolutionary
Propensity Score Matching Methods For Non-Experimental Causal Studies
, 2002
"... This paper considers causal inference and sample selection bias in non-experimental settings in which: (i) few units in the non-experimental comparison group are comparable to the treatment units; and (ii) selecting a subset of comparison units similar to the treatment units is difficult because uni ..."
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Cited by 714 (3 self)
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This paper considers causal inference and sample selection bias in non-experimental settings in which: (i) few units in the non-experimental comparison group are comparable to the treatment units; and (ii) selecting a subset of comparison units similar to the treatment units is difficult because
A comparison of document clustering techniques
- In KDD Workshop on Text Mining
, 2000
"... This paper presents the results of an experimental study of some common document clustering techniques: agglomerative hierarchical clustering and K-means. (We used both a “standard” K-means algorithm and a “bisecting ” K-means algorithm.) Our results indicate that the bisecting K-means technique is ..."
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Cited by 613 (27 self)
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This paper presents the results of an experimental study of some common document clustering techniques: agglomerative hierarchical clustering and K-means. (We used both a “standard” K-means algorithm and a “bisecting ” K-means algorithm.) Our results indicate that the bisecting K-means technique
The Aurora Experimental Framework for the Performance Evaluation of Speech Recognition Systems under Noisy Conditions
- in ISCA ITRW ASR2000
, 2000
"... This paper describes a database designed to evaluate the performance of speech recognition algorithms in noisy conditions. The database may either be used to measure frontend feature extraction algorithms, using a defined HMM recognition back-end, or complete recognition systems. The source speech f ..."
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Cited by 534 (6 self)
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being used to evaluate alternative proposals for front-end feature extraction. The database has been made publicly available through ELRA so that other speech researchers to evaluate and compare the performance of noise robust algorithms. Recognition results will be presented for the first standard DSR
A training algorithm for optimal margin classifiers
- PROCEEDINGS OF THE 5TH ANNUAL ACM WORKSHOP ON COMPUTATIONAL LEARNING THEORY
, 1992
"... A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjust ..."
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Cited by 1865 (43 self)
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-dimension are given. Experimental results on optical character recognition problems demonstrate the good generalization obtained when compared with other learning algorithms.
Results 1 - 10
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276,815
