نتایج جستجو برای: marquardt levenberg

تعداد نتایج: 2083  

2016
Tapas Si

This paper proposes a novel application of Grammatical Bee Colony for classification of medical data. Grammatical Bee Colony is a Swarm Programming algorithm generally used for automatic computer program generation in any arbitrary language. In this paper, Grammatical Bee Colony based classifier is designed and applied in medical data mining. The proposed method is applied on ten medical data s...

Journal: :Numerische Mathematik 2010
Marlis Hochbruck Michael Hönig

In this note we study the convergence of the Levenberg-Marquardt regularization scheme for nonlinear ill-posed problems. We consider the case that the initial error satisfies a source condition. Our main result shows that if the regularization parameter does not grow too fast (not faster than a geometric sequence), then the scheme converges with optimal convergence rates. Our analysis is based ...

2002
Gordon K. Smyth

This paper considers REML (residual or restricted maximum likelihood) estimation for heteroscedastic linear models. An explicit algorithm is given for REML-scoring which yields the REML estimates together with their standard errors and likelihood values. The algorithm includes a Levenberg-Marquardt restricted step modification which ensures that the REML-likelihood increases at each iteration. ...

2014
Michael Manhart Andreas K. Maier Joachim Hornegger Arnd Doerfler

Introduction: Energy resolving X-ray photon counting detectors are able to assign each detected photon to energy bins [1] (Figure 1). This allows the decomposition of an X-ray image into the materials of the acquired object [1, 2], which has potential benefits in angiography. Contrast agent can be extracted without acquiring a mask image, possibly saving dose and avoiding problems with patient ...

Journal: :Computational Statistics & Data Analysis 2007
Josef Tvrdík Ivan Krivý Ladislav Misík

Algorithms for the estimation of nonlinear regression parameters are considered. Adaptive population-based search algorithms are proposed and implemented in deriving reliable estimates at a reasonable time with default setting of their controlling parameters. The algorithms are tested on the NIST collection of datasets containing 27 nonlinear regression tasks of various level of difficulty. The...

2013
Melvin Deloyd Robinson Michael T. Manry

In this paper, we develop and demonstrate a new 2 order two-stage algorithm called OWO-Newton. First, two-stage algorithms are motivated and the Gauss Newton input weight Hessian matrix is developed. Block coordinate descent is used to apply Newton’s algorithm alternately to the input and output weights. Its performance is comparable to Levenberg-Marquardt and it has the advantage of reduced co...

2017
L. Han M. Kamber

This paper proposes a novel application of Grammatical Bee Colony for classification of medical data. Grammatical Bee Colony is a Swarm Programming algorithm generally used for automatic computer program generation in any arbitrary language. In this paper, Grammatical Bee Colony based classifier is designed and applied in medical data mining. The proposed method is applied on ten medical data s...

Journal: :Comp. Opt. and Appl. 2016
Elizabeth W. Karas Sandra A. Santos Benar Fux Svaiter

A class of Levenberg-Marquardt methods for solving the nonlinear least-squares problem is proposed with algebraic explicit rules for computing the regularization parameter. The convergence properties of this class of methods are analyzed. All accumulation points of the generated sequence are proved to be stationary. Q-quadratic rate of convergence for the zero-residual problem is obtained under...

2013
Sriparna Saha Monalisa Pal Amit Konar Ramadoss Janarthanan

A simple method to detect gestures revealing muscle and joint pain is described in this paper. Kinect Sensor is used for data acquisition. This sensor only processes twenty joint coordinates in three dimensional space for feature extraction. The recognition part is achieved using a neural network optimized by Levenberg-Marquardt learning rule. A high recognition rate of 91.9% is achieved using ...

Journal: :Neural computation 2002
Nicol N. Schraudolph

We propose a generic method for iteratively approximating various second-order gradient steps - Newton, Gauss-Newton, Levenberg-Marquardt, and natural gradient - in linear time per iteration, using special curvature matrix-vector products that can be computed in O(n). Two recent acceleration techniques for on-line learning, matrix momentum and stochastic meta-descent (SMD), implement this appro...

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