Results 1 - 10 of 66,989

The Mathematics of Statistical Machine Translation: Parameter Estimation

by Peter F. Brown, Stephen A. Della Pietra, Vincent J. Della Pietra, Robert. L. Mercer - COMPUTATIONAL LINGUISTICS , 1993
"... ..."
Abstract - Cited by 1555 (1 self) - Add to MetaCart
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A gentle tutorial on the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models

by Jeff A. Bilmes , 1997
"... We describe the maximum-likelihood parameter estimation problem and how the Expectation-form of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture of Gaussian densities, and 2) fi ..."
Abstract - Cited by 693 (4 self) - Add to MetaCart
We describe the maximum-likelihood parameter estimation problem and how the Expectation-form of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture of Gaussian densities, and 2

A Comparison of Algorithms for Maximum Entropy Parameter Estimation

by Robert Malouf
"... A comparison of algorithms for maximum entropy parameter estimation Conditional maximum entropy (ME) models provide a general purpose machine learning technique which has been successfully applied to fields as diverse as computer vision and econometrics, and which is used for a wide variety of class ..."
Abstract - Cited by 290 (2 self) - Add to MetaCart
A comparison of algorithms for maximum entropy parameter estimation Conditional maximum entropy (ME) models provide a general purpose machine learning technique which has been successfully applied to fields as diverse as computer vision and econometrics, and which is used for a wide variety

Parameter Estimation

by L. G. De Pillis, A. E. Radunskaya , 2002
"... using measured steady-state values. 8. Parameter estimation without an explicit solution. 9. Demonstration of the procedure, and results. 1 ..."
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using measured steady-state values. 8. Parameter estimation without an explicit solution. 9. Demonstration of the procedure, and results. 1

for parameter estimation

by Myriam Lazard, Ste Phane Andre, Denis Maillet, Alain Degiovanni , 1999
"... Abstract. The measurement of the intrinsic diffusivity of a semitransparent sample is investigated by means of the flash method. A precise analytical model of the combined transient conductive and radiative transfer (quadrupole formulation) is used for the parameter estimation problem. Maps of sensi ..."
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Abstract. The measurement of the intrinsic diffusivity of a semitransparent sample is investigated by means of the flash method. A precise analytical model of the combined transient conductive and radiative transfer (quadrupole formulation) is used for the parameter estimation problem. Maps

Parameter estimation for text analysis

by Gregor Heinrich , 2004
"... Abstract. Presents parameter estimation methods common with discrete probability distributions, which is of particular interest in text modeling. Starting with maximum likelihood, a posteriori and Bayesian estimation, central concepts like conjugate distributions and Bayesian networks are reviewed. ..."
Abstract - Cited by 119 (0 self) - Add to MetaCart
Abstract. Presents parameter estimation methods common with discrete probability distributions, which is of particular interest in text modeling. Starting with maximum likelihood, a posteriori and Bayesian estimation, central concepts like conjugate distributions and Bayesian networks are reviewed

PARAMETER ESTIMATION by

by Elias Kyriakides, Elias Kyriakides , 2003
"... A method to identify synchronous generator parameters from on-line measurements is presented. Generator parameters are employed in the construction of models used in transient stability studies and other routine power engineering studies. These studies are critical for the operation of the power sys ..."
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internal temperature, magnetic saturation, and coupling between the generator and external systems. The method proposed in this dissertation estimates generator parameters at any operating level, taking into consideration the effect of saturation and other phenomena in the operation of the synchronous

Robust parameter estimation in computer vision

by Charles V. Stewart - SIAM Reviews , 1999
"... Abstract. Estimation techniques in computer vision applications must estimate accurate model parameters despite small-scale noise in the data, occasional large-scale measurement errors (outliers), and measurements from multiple populations in the same data set. Increasingly, robust estimation techni ..."
Abstract - Cited by 164 (10 self) - Add to MetaCart
Abstract. Estimation techniques in computer vision applications must estimate accurate model parameters despite small-scale noise in the data, occasional large-scale measurement errors (outliers), and measurements from multiple populations in the same data set. Increasingly, robust estimation

Parameter estimation for linear dynamical systems

by Zoubin Ghahramani, Geoffrey E. Hinton , 1996
"... Linear systems have been used extensively in engineering to model and control the behavior of dynamical systems. In this note, we present the Expectation Maximization (EM) algorithm for estimating the parameters of linear systems (Shumway and Stoffer, 1982). We also point out the relationship betwee ..."
Abstract - Cited by 195 (8 self) - Add to MetaCart
Linear systems have been used extensively in engineering to model and control the behavior of dynamical systems. In this note, we present the Expectation Maximization (EM) algorithm for estimating the parameters of linear systems (Shumway and Stoffer, 1982). We also point out the relationship

Parameter Estimation Techniques: A Tutorial with Application to Conic Fitting

by Zhengyou Zhang , 1995
"... Almost all problems in computer vision are related in one form or another to the problem of estimating parameters from noisy data. In this tutorial, we present what is probably the most commonly used techniques for parameter estimation. These include linear least-squares (pseudo-inverse and eigen a ..."
Abstract - Cited by 278 (8 self) - Add to MetaCart
Almost all problems in computer vision are related in one form or another to the problem of estimating parameters from noisy data. In this tutorial, we present what is probably the most commonly used techniques for parameter estimation. These include linear least-squares (pseudo-inverse and eigen
Results 1 - 10 of 66,989