Results 1  10
of
1,291
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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. For each experimental run, we first gen erated random CPTs. We then sampled from the joint distribution defined by the network and clamped the observed nodes (all nodes in the bottom layer) to their sampled value. Given a structure and observations, we then ran three inference algorithms junction tree
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
Stochastic Gradient Boosting
 Computational Statistics and Data Analysis
, 1999
"... Gradient boosting constructs additive regression models by sequentially fitting a simple parameterized function (base learner) to current "pseudo"residuals by leastsquares at each iteration. The pseudoresiduals are the gradient of the loss functional being minimized, with respect to ..."
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Cited by 285 (1 self)
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data is drawn at random (without replacement) from the full training data set. This randomly selected subsample is then used in place of the full sample to fit the base learner and compute the model update for the current iteration. This randomized approach also increases robustness against
Correcting sample selection bias by unlabeled data
"... We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recover sampling distributions and then make appropriate corrections based on the distribution estimate. We prese ..."
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Cited by 207 (11 self)
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We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recover sampling distributions and then make appropriate corrections based on the distribution estimate. We
Developments in the Measurement of Subjective WellBeing
 Psychological Science.
, 1993
"... F or good reasons, economists have had a longstanding preference for studying peoples' revealed preferences; that is, looking at individuals' actual choices and decisions rather than their stated intentions or subjective reports of likes and dislikes. Yet people often make choices that b ..."
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Cited by 284 (7 self)
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desire loses some of its appeal. Direct reports of subjective wellbeing may have a useful role in the measurement of consumer preferences and social welfare, if they can be done in a credible way. Indeed, economists have already made much use of subjective wellbeing data. From 2001 to 2005, more than
Sketching sampled data streams
 In ICDE
, 2009
"... Abstract—Sampling is used as a universal method to reduce the running time of computations – the computation is performed on a much smaller sample and then the result is scaled to compensate for the difference in size. Sketches are a popular approximation method for data streams and they proved to b ..."
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Cited by 4 (0 self)
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with replacement which is used to generate i.i.d. samples from a distribution, and sampling without replacement which is used by online aggregation engines – and compare these particular results with the results of the basic sketch estimator. Our experimental results show that the accuracy of the sketch computed
Automated Extraction and Parameterization of Motions in Large Data Sets
 ACM Transactions on Graphics
, 2004
"... Large motion data sets often contain many variants of the same kind of motion, but without appropriate tools it is difficult to fully exploit this fact. This paper provides automated methods for identifying logically similar motions in a data set and using them to build a continuous and intuitively ..."
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Cited by 183 (2 self)
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Large motion data sets often contain many variants of the same kind of motion, but without appropriate tools it is difficult to fully exploit this fact. This paper provides automated methods for identifying logically similar motions in a data set and using them to build a continuous and intuitively
Optimal sampling from distributed streams
 Proc. ACM Symposium on Principles of Database Systems
, 2009
"... A fundamental problem in data management is to draw a sample of a large data set, for approximate query answering, selectivity estimation, and query planning. With large, streaming data sets, this problem becomes particularly difficult when the data is shared across multiple distributed sites. The c ..."
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Cited by 23 (7 self)
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for each participant. In this paper, we present communicationefficient protocols for sampling (both with and without replacement) from k distributed streams. These apply to the case when we want a sample from the full streams, and to the sliding window cases of only the W most recent items, or arrivals
Coil sensitivity encoding for fast MRI. In:
 Proceedings of the ISMRM 6th Annual Meeting,
, 1998
"... New theoretical and practical concepts are presented for considerably enhancing the performance of magnetic resonance imaging (MRI) by means of arrays of multiple receiver coils. Sensitivity encoding (SENSE) is based on the fact that receiver sensitivity generally has an encoding effect complementa ..."
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Cited by 193 (3 self)
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reconstruction from multiple receiver data. Using the framework of linear algebra, two different reconstruction strategies have been derived. In their general forms the resulting formulae hold for arbitrary sampling patterns in kspace. A detailed discussion is dedicated to the most practical case, namely
Continuous Sampling from Distributed Streams
, 2011
"... A fundamental problem in data management is to draw and maintain a sample of a large data set, for approximate query answering, selectivity estimation, and query planning. With large, streaming data sets, this problem becomes particularly difficult when the data is shared across multiple distributed ..."
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Cited by 7 (3 self)
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for each participant. In this paper, we present communicationefficient protocols for continuously maintaining a sample (both with and without replacement) from k distributed streams. These apply to the case when we want a sample from the full streams, and to the sliding window cases of only the W most
Results 1  10
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1,291