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On the Complexity of Learning from Drifting Distributions
 In Proceedings of the Workshop on Computational Learning Theory
, 1996
"... We consider two models of online learning of binaryvalued functions from drifting distributions due to Bartlett. We show that if each example is drawn from a joint distribution which changes in total variation distance by at most O(ffl 3 =(d log(1=ffl))) between trials, then an algorithm can ach ..."
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Cited by 12 (0 self)
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We consider two models of online learning of binaryvalued functions from drifting distributions due to Bartlett. We show that if each example is drawn from a joint distribution which changes in total variation distance by at most O(ffl 3 =(d log(1=ffl))) between trials, then an algorithm can
Active Learning with a Drifting Distribution
"... Abstract. We study the problem of active learning in a streambased setting, allowing the distribution of the examples to change over time. We prove upper bounds on the number of prediction mistakes and number of label requests for established disagreementbased active learning algorithms, both in t ..."
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Cited by 1 (0 self)
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Abstract. We study the problem of active learning in a streambased setting, allowing the distribution of the examples to change over time. We prove upper bounds on the number of prediction mistakes and number of label requests for established disagreementbased active learning algorithms, both
New Analysis and Algorithm for Learning with Drifting Distributions
"... Abstract. We present a new analysis of the problem of learning with drifting distributions in the batch setting using the notion of discrepancy. We prove learning bounds based on the Rademacher complexity of the hypothesis set and the discrepancy of distributions both for a drifting PAC scenario and ..."
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Cited by 6 (4 self)
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Abstract. We present a new analysis of the problem of learning with drifting distributions in the batch setting using the notion of discrepancy. We prove learning bounds based on the Rademacher complexity of the hypothesis set and the discrepancy of distributions both for a drifting PAC scenario
Inference in Linear Time Series Models with Some Unit Roots,”
 Econometrica
, 1990
"... This paper considers estimation and hypothesis testing in linear time series models when some or all of the variables have unit roots. Our motivating example is a vector autoregression with some unit roots in the companion matrix, which might include polynomials in time as regressors. In the genera ..."
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Cited by 390 (14 self)
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. In the general formulation, the variable might be integrated or cointegrated of arbitrary orders, and might have drifts as well. We show that parameters that can be written as coefficients on mean zero, nonintegrated regressors have jointly normal asymptotic distributions, converging at the rate T'/2
The Variance Gamma Process and Option Pricing.
 European Finance Review
, 1998
"... : A three parameter stochastic process, termed the variance gamma process, that generalizes Brownian motion is developed as a model for the dynamics of log stock prices. The process is obtained by evaluating Brownian motion with drift at a random time given by a gamma process. The two additional par ..."
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Cited by 365 (34 self)
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parameters are the drift of the Brownian motion and the volatility of the time change. These additional parameters provide control over the skewness and kurtosis of the return distribution. Closed forms are obtained for the return density and the prices of European options. The statistical and risk neutral
Drifts and volatilities: Monetary policies and outcomes
 in the post World War II US. Review of Economic Dynamics
, 2005
"... For a VAR with drifting coefficients and stochastic volatilities, we present posterior densities for several objects that are of interest for designing and evaluating monetary policy. These include measures of inflation persistence, the naturalrateofunemployment,acorerateofinflation, and ‘activism c ..."
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Cited by 182 (4 self)
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For a VAR with drifting coefficients and stochastic volatilities, we present posterior densities for several objects that are of interest for designing and evaluating monetary policy. These include measures of inflation persistence, the naturalrateofunemployment,acorerateofinflation, and ‘activism
Learning with drift detection
 In SBIA Brazilian Symposium on Artificial Intelligence
, 2004
"... Abstract. Most of the work in machine learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem of learning when the distribution that generate the examples changes over time. We present a method for detection of ch ..."
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Cited by 91 (7 self)
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of changes in the probability distribution of examples. The idea behind the drift detection method is to control the online errorrate of the algorithm. The training examples are presented in sequence. When a new training example is available, it is classified using the actual model. Statistical theory
The Timed Asynchronous Distributed System Model
, 1999
"... We propose a formal definition for the timed asynchronous distributed system model. We present extensive measurements of actual message and process scheduling delays and hardware clock drifts. These measurements confirm that this model adequately describes current distributed systems such as a netwo ..."
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Cited by 191 (19 self)
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We propose a formal definition for the timed asynchronous distributed system model. We present extensive measurements of actual message and process scheduling delays and hardware clock drifts. These measurements confirm that this model adequately describes current distributed systems such as a
Detecting Concept Drift with Support Vector Machines
 In Proceedings of the Seventeenth International Conference on Machine Learning (ICML
, 2000
"... For many learning tasks where data is collected over an extended period of time, its underlying distribution is likely to change. A typical example is information filtering, i.e. the adaptive classification of documents with respect to a particular user interest. Both the interest of the user and th ..."
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Cited by 124 (8 self)
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For many learning tasks where data is collected over an extended period of time, its underlying distribution is likely to change. A typical example is information filtering, i.e. the adaptive classification of documents with respect to a particular user interest. Both the interest of the user
The Problem of Concept Drift: Definitions and Related Work
, 2004
"... In the real world concepts are often not stable but change with time. Typical examples of this are weather prediction rules and customers' preferences. The underlying data distribution may change as well. Often these changes make the model built on old data inconsistent with the new data, and r ..."
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Cited by 110 (5 self)
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In the real world concepts are often not stable but change with time. Typical examples of this are weather prediction rules and customers' preferences. The underlying data distribution may change as well. Often these changes make the model built on old data inconsistent with the new data
Results 1  10
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2,480