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3,017
Inducing Features of Random Fields
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1997
"... We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the ..."
Abstract

Cited by 670 (10 self)
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We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing
Fast texture synthesis using treestructured vector quantization
, 2000
"... Figure 1: Our texture generation process takes an example texture patch (left) and a random noise (middle) as input, and modifies this random noise to make it look like the given example texture. The synthesized texture (right) can be of arbitrary size, and is perceived as very similar to the given ..."
Abstract

Cited by 561 (12 self)
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, but runs two orders of magnitude faster. This permits us to apply texture synthesis to problems where it has traditionally been considered impractical. In particular, we have applied it to constrained synthesis for image editing and temporal texture generation. Our algorithm is derived from Markov Random
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 ..."
Abstract

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
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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of context, Schwarz (1987) invited subjects to the lab to fill out a questionnaire on life satisfaction. Before they answered the questionnaire, however, he asked them to photocopy a sheet of paper for him. A dime was placed on the copy machine for a randomly chosen half of the sample. Reported satisfaction
2004b), IntCal04 terrestrial radiocarbon age calibration
 Cal Kyr BP., Radiocarbon
"... ABSTRACT. A new calibration curve for the conversion of radiocarbon ages to calibrated (cal) ages has been constructed and internationally ratified to replace IntCal98, which extended from 0–24 cal kyr BP (Before Present, 0 cal BP = AD 1950). The new calibration data set for terrestrial samples exte ..."
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Cited by 222 (1 self)
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extends from 0–26 cal kyr BP, but with much higher resolution beyond 11.4 cal kyr BP than IntCal98. Dendrochronologicallydated treering samples cover the period from 0–12.4 cal kyr BP. Beyond the end of the tree rings, data from marine records (corals and foraminifera) are converted to the atmospheric
Stock prices and top management changes
 Journal of Financial Economics
, 1988
"... This p~er studies the association between a finn's stock returns and subsequent top management chAMes. Con~ieut w~th mmml monitoring of management, there is an inverse relation between the probability of a management change and a firm's share performance. ~ relation can result from monitor ..."
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Cited by 240 (1 self)
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top management change is inversely related to stock price performance. Using a random sample of listed firms, the hypothesis is tested with a prediction procedure to exploit information on firms that do not experience a management change. In addition, standard event study methodology is employed
MAP estimation via agreement on trees: Messagepassing and linear programming
, 2002
"... We develop and analyze methods for computing provably optimal maximum a posteriori (MAP) configurations for a subclass of Markov random fields defined on graphs with cycles. By decomposing the original distribution into a convex combination of treestructured distributions, we obtain an upper bound ..."
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Cited by 191 (9 self)
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is that any such shared configuration must also be a MAP configuration for the original distribution. Next we develop two approaches to attempting to obtain tight upper bounds: (a) a treerelaxed linear program (LP), which is derived from the Lagrangian dual of the upper bounds; and (b) a treereweighted max
How to Get a Perfectly Random Sample from a Generic Markov Chain and Generate a Random Spanning Tree of a Directed Graph
 JOURNAL OF ALGORITHMS
, 1998
"... A general problem in computational probability theory is that of generating a random sample from the state space of a Markov chain in accordance with the steadystate probability law of the chain. Another problem is that of generating a random spanning tree of a graph or spanning arborescence of a d ..."
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Cited by 109 (7 self)
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A general problem in computational probability theory is that of generating a random sample from the state space of a Markov chain in accordance with the steadystate probability law of the chain. Another problem is that of generating a random spanning tree of a graph or spanning arborescence of a
Bayesian phylogenetic inference via Markov chain Monte Carlo methods
 Biometrics
, 1999
"... SUMMARY. We derive a Markov chain to sample from the posterior distribution for a phylogenetic tree given sequence information from the corresponding set of organisms, a stochastic model for these data, and a prior distribution on the space of trees. A transformation of the tree into a canonical cop ..."
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Cited by 159 (6 self)
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SUMMARY. We derive a Markov chain to sample from the posterior distribution for a phylogenetic tree given sequence information from the corresponding set of organisms, a stochastic model for these data, and a prior distribution on the space of trees. A transformation of the tree into a canonical
Results 1  10
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