Results 271 to 280 of about 441,425 (328)

Uniqueness characteristics of the 2-D IIR mean squared error minimization

Proceedings of 1st International Conference on Image Processing, 2002
Several different two-dimensional adaptive filter structures and algorithms have been developed, the most recent of which is a 2D IIR adaptive filter. Two-dimensional IIR adaptive filters can be used in a variety of image and video processing applications.
J.C. Strait, W.K. Jenkins
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Algorithm for sparse representation minimizing mean square error of power spectrograms

2015 IEEE International Conference on Digital Signal Processing (DSP), 2015
Sparse representation is an idea to approximate a target signal by a linear combination of a small number of sample signals, and it is utilized in various research fields. In this paper, we evaluate the approximation error of signals by the mean square error of power spectrograms (P-MSE).
Yuma Tanaka, Takahiro Ogawa, Miki Haseyama   +2 more
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Bias Adjustment Minimizing the Asymptotic Mean Square Error

Communications in Statistics - Theory and Methods, 2013
A method of bias adjustment which minimizes the asymptotic mean square error is presented for an estimator typically given by maximum likelihood. Generally, this adjustment includes unknown population values. However, in some examples, the adjustment can be done without population values.
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Supermodular mean squared error minimization for sensor scheduling in optimal Kalman Filtering

2017 American Control Conference (ACC), 2017
We consider the problem of scheduling a set of sensors to observe the state of a discrete-time linear system subject to a limited energy budget. Our goal is to devise a sensor schedule that minimizes the mean squared error (MSE) of an optimal estimator (i.e., the Kalman Filter). Both the minimum-MSE and the minimum-cardinality optimal sensor scheduling
Prince Singh, Min Chen, Luca Carlone, Sertac Karaman, Emilio Frazzoli, David Hsu   +5 more
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An iterative approach to minimize the mean squared error in ridge regression

Computational Statistics, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wong, Ka Yiu, Chiu, Sung Nok
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PtNLMS algorithm obtained by minimization of mean square error modeled by exponential functions

2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers, 2010
Using the proportionate-type steepest descent algorithm we represent the current weight deviations in terms of initial weight deviations. Then we attempt to minimize the mean square output error with respect to the gains at a given instant. The corresponding optimal average gains are found using a water-filling procedure.
Kevin T. Wagner, Milos I. Doroslovacki
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Linear model averaging by minimizing mean-squared forecast error unbiased estimator

Model Assisted Statistics and Applications, 2016
This paper presents a new ordinary least squares model averaging method which is proposed to be a preferable alternative to Mallows Model Averaging (MMA), Bayesian Model Averaging (BMA) and naïve simple forecast average. The method is developed to deal with possibly non-nested models and selects forecast weights by minimizing the unbiased estimator of ...
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Filter Bank Design for Minimizing Mean-Squared Estimation Error in Subband Adaptive Filtering

Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005., 2006
This paper considers the problem of prototype filter design for subband adaptive filtering applied to system identification. The minimum mean-squared estimation error (MMSE) depends only the subband analysis filter and the response of the unknown system. We use MMSE as a design criterion to select the best analysis filter response. We show how this can
J. Gunther, T. Bose, W. Song
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Robust Least-Squares Support Vector Machine With Minimization of Mean and Variance of Modeling Error

IEEE Transactions on Neural Networks and Learning Systems, 2017
The least-squares support vector machine (LS-SVM) is a popular data-driven modeling method and has been successfully applied to a wide range of applications. However, it has some disadvantages, including being ineffective at handling non-Gaussian noise as well as being sensitive to outliers.
Xinjiang Lu, Wenbo Liu, Chuang Zhou, Minghui Huang   +3 more
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Minimizing average mean squared error: a comparison of preliminary test estimation to other procedures

Journal of Statistical Computation and Simulation, 1978
The approach of preliminary test estimation has been suggested by Ellerton and Myers (1977) as a means for controlling the size of the J-criterion of the estimator ŷ of the true response,ηIn this paper, the j-criterion associated with the response estimator employed by Ellerton and Myers is evaluated and comparisons made with the j-criteria of several ...
Thomas B. Vassar, Walter H. Carter
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