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Markov Random Field Models in Computer Vision
, 1994
"... . A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is defined as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model. The l ..."
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Cited by 516 (18 self)
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. The latter relates to how data is observed and is problem domain dependent. The former depends on how various prior constraints are expressed. Markov Random Field Models (MRF) theory is a tool to encode contextual constraints into the prior probability. This paper presents a unified approach for MRF modeling
Mining timechanging data streams
 IN PROC. OF THE 2001 ACM SIGKDD INTL. CONF. ON KNOWLEDGE DISCOVERY AND DATA MINING
, 2001
"... Most statistical and machinelearning algorithms assume that the data is a random sample drawn from a stationary distribution. Unfortunately, most of the large databases available for mining today violate this assumption. They were gathered over months or years, and the underlying processes genera ..."
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Cited by 338 (5 self)
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Most statistical and machinelearning algorithms assume that the data is a random sample drawn from a stationary distribution. Unfortunately, most of the large databases available for mining today violate this assumption. They were gathered over months or years, and the underlying pro
The Determinants of Credit Spread Changes.
 Journal of Finance
, 2001
"... ABSTRACT Using dealer's quotes and transactions prices on straight industrial bonds, we investigate the determinants of credit spread changes. Variables that should in theory determine credit spread changes have rather limited explanatory power. Further, the residuals from this regression are ..."
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Cited by 422 (2 self)
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spreads. Changes in Business Climate We use monthly S&P 500 returns, S&P t , as a proxy for the overall state of the economy. The data are obtained from CRSP. For ease of analysis, each bond is assigned to a leverage group based on the firm's average leverage ratio for those months where
Segmentation using eigenvectors: A unifying view
 In ICCV
, 1999
"... Automatic grouping and segmentation of images remains a challenging problem in computer vision. Recently, a number of authors have demonstrated good performance on this task using methods that are based on eigenvectors of the a nity matrix. These approaches are extremely attractive in that they are ..."
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Cited by 380 (1 self)
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Automatic grouping and segmentation of images remains a challenging problem in computer vision. Recently, a number of authors have demonstrated good performance on this task using methods that are based on eigenvectors of the a nity matrix. These approaches are extremely attractive
Mining ConceptDrifting Data Streams Using Ensemble Classifiers
, 2003
"... Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud protection, target marketing, network intrusion detection, etc. Conventional knowledge discovery tools are facing two ch ..."
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Cited by 280 (37 self)
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Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud protection, target marketing, network intrusion detection, etc. Conventional knowledge discovery tools are facing two
A MatrixBased Approach to Global Locality Optimization
, 1998
"... Global locality optimization is a technique for improving the cache performance of a sequence of loop nests through a combination of loop and data layout transformations. Pure loop transformations are restricted by data dependences and may not be very successful in optimizing imperfectly nested loop ..."
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Cited by 35 (10 self)
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are propagated for the optimizing the remaining loop nests. We show a simple and effective matrixbased framework to implement this process. The search space that we consider for possible loop transformations can be represented by general nonsingular linear transformation matrices and the data layouts that we
Razor: A lowpower pipeline based on circuitlevel timing speculation
 in Proc. IEEE/ACM Int. Symp. Microarchitect
, 2003
"... With increasing clock frequencies and silicon integration, power aware computing has become a critical concern in the design of embedded processors and systemsonchip. One of the more effective and widely used methods for poweraware computing is dynamic voltage scaling (DVS). In order to obtain the ..."
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Cited by 288 (8 self)
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, this approach leads to a very conservative supply voltage since such a worstcase combination of different variabilities will be very rare. In this paper, we propose a new approach to DVS, called Razor, based on dynamic detection and correction of circuit timing errors. The key idea of Razor is to tune
The LRPD Test: Speculative RunTime Parallelization of Loops with Privatization and Reduction Parallelization
, 1995
"... Current parallelizing compilers cannot identify a significant fraction of parallelizable loops because they have complex or statically insufficiently defined access patterns. As parallelizable loops arise frequently in practice, we advocate a novel framework for their identification: speculatively e ..."
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Cited by 261 (44 self)
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execute the loop as a doall, and apply a fully parallel data dependence test to determine if it had any crossiteration dependences; if the test fails, then the loop is reexecuted serially. Since, from our experience, a significant amount of the available parallelism in Fortran programs can
An Empirical Bayes Approach to Inferring LargeScale Gene Association Networks
 BIOINFORMATICS
, 2004
"... Motivation: Genetic networks are often described statistically by graphical models (e.g. Bayesian networks). However, inferring the network structure offers a serious challenge in microarray analysis where the sample size is small compared to the number of considered genes. This renders many standar ..."
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Cited by 237 (6 self)
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) that are now frequently used to describe gene association networks and to detect conditionally dependent genes. Our new approach is based on (i) improved (regularized) smallsample point estimates of partial correlation, (ii) an exact test of edge inclusion with adaptive estimation of the degree of freedom
On the Nyström Method for Approximating a Gram Matrix for Improved KernelBased Learning
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2005
"... A problem for many kernelbased methods is that the amount of computation required to find the solution scales as O(n³), where n is the number of training examples. We develop and analyze an algorithm to compute an easilyinterpretable lowrank approximation to an nn Gram matrix G such that compu ..."
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Cited by 188 (11 self)
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and the corresponding c rows of G. An important aspect of the algorithm is the probability distribution used to randomly sample the columns; we will use a judiciouslychosen and datadependent nonuniform probability distribution. Let F denote the spectral norm and the Frobenius norm, respectively, of a matrix
Results 1  10
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