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The Nature of Statistical Learning Theory
, 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
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Cited by 13236 (32 self)
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Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based
Maximum likelihood from incomplete data via the EM algorithm
 JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 1977
"... A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situat ..."
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Cited by 11972 (17 self)
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A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value
Tracking characteristics of an OBE parameter estimation algorithm
 IEEE Trans. Signal Processing
, 1993
"... AbstmctRecently there seems to have been a resurgence of interest in recursive parameterbounding algorithms. These algorithms are applicable when the noise is bounded and the bound is known to the user. One of the advantages of such algorithms is that 100 % confidence regions (which are optimal in ..."
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Cited by 10 (7 self)
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in some sense) for the parameter estimates can be obtained at every time instant, rather than asymptotically as in the case of the least squares type algorithms. Another advantage is that these recursive algorithms have the inherent capability of implementing discerning updates, particularly
Optimizing qubit Hamiltonian parameter estimation algorithms using
 PSO, Proceedings of 2012 IEEE Conference on Evolutionary Computation (CEC
"... ar ..."
A combined order selection and parameter estimation algorithm for undamped exponentials
 IEEE Trans. Signal Process
, 2000
"... Abstract—We propose an approximate maximum likelihood parameter estimation algorithm, combined with a model order estimator, for superimposed undamped exponentials in noise. The algorithm combines the robustness of Fourierbased estimators and the highresolution capabilities of parametric methods. ..."
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Cited by 2 (0 self)
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Abstract—We propose an approximate maximum likelihood parameter estimation algorithm, combined with a model order estimator, for superimposed undamped exponentials in noise. The algorithm combines the robustness of Fourierbased estimators and the highresolution capabilities of parametric methods
Parameter Estimation Algorithm for the Exponential Signal by the Enhanced DFT Approach
, 2014
"... Based on enhanced interpolation DFT, a novel parameter estimation algorithm for the exponential signal is presented. The proposed twostep solution consists of a preprocessing unit which constructs a new signal sequence by continuously cycle shifting sample points and summing up N buffered exponent ..."
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Based on enhanced interpolation DFT, a novel parameter estimation algorithm for the exponential signal is presented. The proposed twostep solution consists of a preprocessing unit which constructs a new signal sequence by continuously cycle shifting sample points and summing up N buffered
WINDUP PROPERTIES OF RECURSIVE PARAMETER ESTIMATION ALGORITHMS IN ACOUSTIC ECHO CANCELLATION
"... Abstract: The windup properties of a recently suggested recursive parameter estimation algorithm are investigated in comparison with a number of wellknown techniques such as the Normalized Least Squares Algorithm (NLMS) and the Kalman filter (KF). An acoustic echo cancellation application is used a ..."
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Abstract: The windup properties of a recently suggested recursive parameter estimation algorithm are investigated in comparison with a number of wellknown techniques such as the Normalized Least Squares Algorithm (NLMS) and the Kalman filter (KF). An acoustic echo cancellation application is used
Comparative Study of Recursive Parameter Estimation Algorithms with Application to Active Vibration Isolation
, 2004
"... In this paper, adaptive filtering is adopted for active automotive engine vibration isolation where both transient and stationary engine internal excitations as well as structure flexibility are considered. The adaptive filtering problem is formulated using a linear regression model representation. ..."
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Cited by 1 (1 self)
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. This allows for an application of a general family of stateoftheart recursive parameter estimation algorithms. The performance of two specific members of this family has been compared. Those are the wellknown normalised least mean square (NLMS) algorithm and a recently suggested Kalman filter based
Acyclic Discrete Phase Type Distributions: Properties and a Parameter Estimation Algorithm
, 2002
"... This paper provides a detailed study on Discrete Phase Type (DPH) distributions and its acyclic subclass referred to as Acyclic DPH (ADPH). Previously not considered similarities and dierences between DPH and Continuous Phase Type (CPH) distributions are investigated and minimal representations, ..."
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Cited by 36 (13 self)
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algorithm is found to be simple and stable. The algorithm is tested over a benchmark consisting of 10 different continuous distributions. The error resulted when a continuous distribution sampled in discrete points is fitted by a DPH is also considered.
A gentle tutorial on the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models
, 1997
"... We describe the maximumlikelihood parameter estimation problem and how the Expectationform 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 ..."
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Cited by 693 (4 self)
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We describe the maximumlikelihood parameter estimation problem and how the Expectationform 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
Results 1  10
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145,183