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76
Advanced Spectral Methods for Climatic Time Series
, 2001
"... The analysis of uni or multivariate time series provides crucial information to describe, understand, and predict climatic variability. The discovery and implementation of a number of novel methods for extracting useful information from time series has recently revitalized this classical eld of ..."
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Cited by 220 (35 self)
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The analysis of uni or multivariate time series provides crucial information to describe, understand, and predict climatic variability. The discovery and implementation of a number of novel methods for extracting useful information from time series has recently revitalized this classical eld of study. Considerable progress has also been made in interpreting the information so obtained in terms of dynamical systems theory.
Clustering time series from ARMA models with clipped data
, 2004
"... Clustering time series from ARMA models with clipped data ..."
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Cited by 23 (9 self)
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Clustering time series from ARMA models with clipped data
InformationTheoretic Learning
, 1999
"... This chapter seeks to extend the ubiquitous meansquare error criterion (MSE) to cost functions that include more information about the training data. Since the learning process ultimately should transfer the information carried in the data samples onto the system's parameters, a natural goal i ..."
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Cited by 22 (0 self)
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This chapter seeks to extend the ubiquitous meansquare error criterion (MSE) to cost functions that include more information about the training data. Since the learning process ultimately should transfer the information carried in the data samples onto the system's parameters, a natural goal is to find cost functions that directly manipulate information. Hence the name informationtheoretic learning (ITL). In order to be useful, ITL should be independent of the learning machine architecture, and require solely the availability of the data, i.e. it should not require a priori assumptions about the data distributions. The chapter presents our current efforts to develop ITL criteria based on the integration of nonparametric density estimators with Renyi's quadratic entropy definition. As a motivation we start with an application of the MSE to manipulate information using the nonlinear characteristics of the learning machine. This section illustrates the issues faced when we attempt to use...
Maximum entropy for collaborative filtering
 In Proceedings of 20th International Conference on Uncertainty in Artificial Intelligence (UAI’04
, 2004
"... Within the task of collaborative filtering two challenges for computing conditional probabilities exist. First, the amount of training data available is typically sparse with respect to the size of the domain. Thus, support for higherorder interactions is generally not present. Second, the variable ..."
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Cited by 9 (0 self)
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Within the task of collaborative filtering two challenges for computing conditional probabilities exist. First, the amount of training data available is typically sparse with respect to the size of the domain. Thus, support for higherorder interactions is generally not present. Second, the variables that we are conditioning upon vary for each query. That is, users label different variables during each query. For this reason, there is no consistent input to output mapping. To address these problems we purpose a maximum entropy approach using a nonstandard measure of entropy. This approach can be simplified to solving a set of linear equations that can be efficiently solved. 1
manuscript in preparation
, 1996
"... disttibution is unlimited. 1 F EFENSE TECHNICRL INFORIATION CENTER ..."
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Cited by 9 (3 self)
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disttibution is unlimited. 1 F EFENSE TECHNICRL INFORIATION CENTER
Maxallent: Maximizers of all entropies and uncertainty
"... journal homepage: www.elsevier.com/locate/camwa ..."
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MaximumEntropy Spatial Processing of Array Data
 Geophysics
, 1974
"... The procedure of maximumentropy spectral analysis (MESA), used in the processing of time series data, also applies to wavenumber (bearing) analysis of signals received from a spatially distributed linear array of sensors. The method is precisely the use of autoregressive spectral analysis in the ..."
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Cited by 7 (0 self)
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The procedure of maximumentropy spectral analysis (MESA), used in the processing of time series data, also applies to wavenumber (bearing) analysis of signals received from a spatially distributed linear array of sensors. The method is precisely the use of autoregressive spectral analysis in the space dimension rather than in 1 Ime. There are also close links to the predictive deconvolution method used in geophysical work, and to the process of constructing noisewhitening filters in communication theory, as well as to leastsquares model building. In this note, we review the maximumentropy procedure pointing out all these links. The specific algorithm appropriate to a uniformly spaced line array of sensors is given, as well as one possible algorithm for use in the case of nonuniform sensor spacing.
Duality and Convex Programming
, 2010
"... We survey some key concepts in convex duality theory and their application to the analysis and numerical solution of problem archetypes in imaging. ..."
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Cited by 7 (3 self)
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We survey some key concepts in convex duality theory and their application to the analysis and numerical solution of problem archetypes in imaging.