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61,505
Just Relax: Convex Programming Methods for Identifying Sparse Signals in Noise
, 2006
"... This paper studies a difficult and fundamental problem that arises throughout electrical engineering, applied mathematics, and statistics. Suppose that one forms a short linear combination of elementary signals drawn from a large, fixed collection. Given an observation of the linear combination that ..."
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Cited by 496 (2 self)
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. This paper studies a method called convex relaxation, which attempts to recover the ideal sparse signal by solving a convex program. This approach is powerful because the optimization can be completed in polynomial time with standard scientific software. The paper provides general conditions which ensure
Synonyms BiasVariance tradeoffs: Novel Applications Biasvariance tradeoffs, bias plus variance. Definition
, 2007
"... Consider a given random variable F and a random variable that we can modify, ˆ F. We wish to use a sample of ˆ F as an estimate of a sample of F. The mean squared error between such a pair of samples is a sum of four terms. The first term reflects the statistical coupling between F and ˆ F and is c ..."
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broader context and in a variety of situations. We also show, using experiments, how techniques for optimizing biasvariance tradeoffs introduced in machine learning can be applied in novel circumstances to improve the performance of a class of optimization algorithms.
The BiasVariance Tradeoff and the Randomized GACV
 Advances in Neural Information Processing Systems
, 1999
"... We propose a new insample cross validation based method (randomized GACV) for choosing smoothing or bandwidth parameters that govern the biasvariance or fitcomplexity tradeoff in `soft' classification. Soft classification refers to a learning procedure which estimates the probability that an ..."
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Cited by 17 (2 self)
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We propose a new insample cross validation based method (randomized GACV) for choosing smoothing or bandwidth parameters that govern the biasvariance or fitcomplexity tradeoff in `soft' classification. Soft classification refers to a learning procedure which estimates the probability
A firstorder primaldual algorithm for convex problems with applications to imaging
, 2010
"... In this paper we study a firstorder primaldual algorithm for convex optimization problems with known saddlepoint structure. We prove convergence to a saddlepoint with rate O(1/N) in finite dimensions, which is optimal for the complete class of nonsmooth problems we are considering in this paper ..."
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Cited by 435 (20 self)
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In this paper we study a firstorder primaldual algorithm for convex optimization problems with known saddlepoint structure. We prove convergence to a saddlepoint with rate O(1/N) in finite dimensions, which is optimal for the complete class of nonsmooth problems we are considering
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 605 (39 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
A computational approach to edge detection
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1986
"... AbstractThis paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal ..."
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Cited by 4621 (0 self)
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. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussiansmoothed image. We extend this simple detector using operators of several widths to cope
Exploring estimator biasvariance tradeoffs using the uniform CR bound
 IEEE Trans. on Sig. Proc
, 1996
"... We introduce a plane, which we call the deltasigma plane, that is indexed by the norm of the estimator bias gradient and the variance of the estimator. The norm of the bias gradient is related to the maximum variation in the estimator bias function over a neighborhood of parameter space. Using a un ..."
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Cited by 58 (18 self)
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We introduce a plane, which we call the deltasigma plane, that is indexed by the norm of the estimator bias gradient and the variance of the estimator. The norm of the bias gradient is related to the maximum variation in the estimator bias function over a neighborhood of parameter space. Using a
Shallow Parsing with Conditional Random Fields
, 2003
"... Conditional random fields for sequence labeling offer advantages over both generative models like HMMs and classifiers applied at each sequence position. Among sequence labeling tasks in language processing, shallow parsing has received much attention, with the development of standard evaluati ..."
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Cited by 575 (8 self)
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evaluation datasets and extensive comparison among methods. We show here how to train a conditional random field to achieve performance as good as any reported base nounphrase chunking method on the CoNLL task, and better than any reported single model. Improved training methods based on modern
Insiders and Outsiders: The Choice between Informed and Arm'sLength Debt
, 1991
"... While the benefits of bank financing are relatively well understood, the costs are not. This paper argues that while informed banks make flexible financial decisions which prevent a firm's projects from going awry, the cost of this credit is that banks have bargaining power over the firm's ..."
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Cited by 846 (18 self)
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's profits, once projects have begun. The firm's portfolio choice of borrowing source and the choice of priority for its debt claims attempt to optimally circumscribe the powers of banks.
Text Classification from Labeled and Unlabeled Documents using EM
 MACHINE LEARNING
, 1999
"... This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is important because in many text classification problems obtaining training labels is expensive, while large qua ..."
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Cited by 1033 (19 self)
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This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is important because in many text classification problems obtaining training labels is expensive, while large
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