Nonlinear Parameter Estimation In Classification Problems

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Nonlinear Parameter Estimation In Classification Problems

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Author : Kim Louise Blackmore
language : en
Publisher:
Release Date : 1995

Nonlinear Parameter Estimation In Classification Problems written by Kim Louise Blackmore and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Algorithms categories.

Nonlinear Estimation And Classification

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Author : David D. Denison
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11

Nonlinear Estimation And Classification written by David D. Denison and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Mathematics categories.

Researchers in many disciplines face the formidable task of analyzing massive amounts of high-dimensional and highly-structured data. This is due in part to recent advances in data collection and computing technologies. As a result, fundamental statistical research is being undertaken in a variety of different fields. Driven by the complexity of these new problems, and fueled by the explosion of available computer power, highly adaptive, non-linear procedures are now essential components of modern "data analysis," a term that we liberally interpret to include speech and pattern recognition, classification, data compression and signal processing. The development of new, flexible methods combines advances from many sources, including approximation theory, numerical analysis, machine learning, signal processing and statistics. The proposed workshop intends to bring together eminent experts from these fields in order to exchange ideas and forge directions for the future.

Classification Parameter Estimation And State Estimation

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Author : Ferdinand van der Heijden
language : en
Publisher: John Wiley & Sons
Release Date : 2005-06-10

Classification Parameter Estimation And State Estimation written by Ferdinand van der Heijden and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-06-10 with Science categories.

Classification, Parameter Estimation and State Estimation is a practical guide for data analysts and designers of measurement systems and postgraduates students that are interested in advanced measurement systems using MATLAB. 'Prtools' is a powerful MATLAB toolbox for pattern recognition and is written and owned by one of the co-authors, B. Duin of the Delft University of Technology. After an introductory chapter, the book provides the theoretical construction for classification, estimation and state estimation. The book also deals with the skills required to bring the theoretical concepts to practical systems, and how to evaluate these systems. Together with the many examples in the chapters, the book is accompanied by a MATLAB toolbox for pattern recognition and classification. The appendix provides the necessary documentation for this toolbox as well as an overview of the most useful functions from these toolboxes. With its integrated and unified approach to classification, parameter estimation and state estimation, this book is a suitable practical supplement in existing university courses in pattern classification, optimal estimation and data analysis. Covers all contemporary main methods for classification and estimation. Integrated approach to classification, parameter estimation and state estimation Highlights the practical deployment of theoretical issues. Provides a concise and practical approach supported by MATLAB toolbox. Offers exercises at the end of each chapter and numerous worked out examples. PRtools toolbox (MATLAB) and code of worked out examples available from the internet Many examples showing implementations in MATLAB Enables students to practice their skills using a MATLAB environment

Parameter Estimation And Inverse Problems

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Author : Richard C. Aster
language : en
Publisher: Academic Press
Release Date : 2013

Parameter Estimation And Inverse Problems written by Richard C. Aster and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Computers categories.

Preface -- 1. Introduction -- 2. Linear Regression -- 3. Discretizing Continuous Inverse Problems -- 4. Rank Deficiency and Ill-Conditioning -- 5. Tikhonov Regularization -- 6. Iterative Methods -- 7. Other Regularization Techniques -- 8. Fourier Techniques -- 9. Nonlinear Regression -- 10. Nonlinear Inverse Problems -- 11. Bayesian Methods -- Appendix A: Review of Linear Algebra -- Appendix B: Review of Probability and Statistics -- Appendix C: Glossary of Notation -- Bibliography -- IndexLinear Regression -- Discretizing Continuous Inverse Problems -- Rank Deficiency and Ill-Conditioning -- Tikhonov Regularization -- Iterative Methods -- Other Regularization Techniques -- Fourier Techniques -- Nonlinear Regression -- Nonlinear Inverse Problems -- Bayesian Methods.

Numerical Methods For Nonlinear Estimating Equations

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Author : Christopher G. Small
language : en
Publisher: OUP Oxford
Release Date : 2003-10-02

Numerical Methods For Nonlinear Estimating Equations written by Christopher G. Small and has been published by OUP Oxford this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-10-02 with Mathematics categories.

Nonlinearity arises in statistical inference in various ways, with varying degrees of severity, as an obstacle to statistical analysis. More entrenched forms of nonlinearity often require intensive numerical methods to construct estimators, and the use of root search algorithms, or one-step estimators, is a standard method of solution. This book provides a comprehensive study of nonlinear estimating equations and artificial likelihoods for statistical inference. It provides extensive coverage and comparison of hill climbing algorithms, which, when started at points of nonconcavity often have very poor convergence properties, and for additional flexibility proposes a number of modifications to the standard methods for solving these algorithms. The book also extends beyond simple root search algorithms to include a discussion of the testing of roots for consistency, and the modification of available estimating functions to provide greater stability in inference. A variety of examples from practical applications are included to illustrate the problems and possibilities thus making this text ideal for the research statistician and graduate student. This is the latest in the well-established and authoritative Oxford Statistical Science Series, which includes texts and monographs covering many topics of current research interest in pure and applied statistics. Each title has an original slant even if the material included is not specifically original. The authors are leading researchers and the topics covered will be of interest to all professional statisticians, whether they be in industry, government department or research institute. Other books in the series include 23. W.J.Krzanowski: Principles of multivariate analysis: a user's perspective updated edition 24. J.Durbin and S.J.Koopman: Time series analysis by State Space Models 25. Peter J. Diggle, Patrick Heagerty, Kung-Yee Liang, Scott L. Zeger: Analysis of Longitudinal Data 2/e 26. J.K. Lindsey: Nonlinear Models in Medical Statistics 27. Peter J. Green, Nils L. Hjort & Sylvia Richardson: Highly Structured Stochastic Systems 28. Margaret S. Pepe: The Statistical Evaluation of Medical Tests for Classification and Prediction

Neural Network Based State Estimation Of Nonlinear Systems

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Author : Heidar A. Talebi
language : en
Publisher: Springer
Release Date : 2009-12-04

Neural Network Based State Estimation Of Nonlinear Systems written by Heidar A. Talebi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-12-04 with Technology & Engineering categories.

"Neural Network-Based State Estimation of Nonlinear Systems" presents efficient, easy to implement neural network schemes for state estimation, system identification, and fault detection and Isolation with mathematical proof of stability, experimental evaluation, and Robustness against unmolded dynamics, external disturbances, and measurement noises.

A Method For Estimating Nonlinear Class Boundaries In The Classification Problem And Comparison With Other Existing Methods

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Author : Smarajit Bose
language : en
Publisher:
Release Date : 1992

A Method For Estimating Nonlinear Class Boundaries In The Classification Problem And Comparison With Other Existing Methods written by Smarajit Bose and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with categories.

New Algorithms For Nonlinear Least Squares And Bayesian Parameter Estimation

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Author : Warren E. Stewart
language : en
Publisher:
Release Date : 1980

New Algorithms For Nonlinear Least Squares And Bayesian Parameter Estimation written by Warren E. Stewart and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1980 with Bayesian statistical decision theory categories.

Some new algorithms are presented for fitting mathematical models to multiple-response experiments. These algorithms give estimates of the parameters in a user-defined predictor model, and also estimate the parameters of a Gaussian model of the observational error distribution. The development is based on Bayes' theorem, and provides a natural extension of known least-squares estimation methods. Allowance is made for missing values of responses, which occur frequently in practical work.

Control And Estimation Of Distributed Parameter Systems Nonlinear Phenomena

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Author : Wolfgang Desch
language : en
Publisher: Birkhäuser
Release Date : 2012-12-06

Control And Estimation Of Distributed Parameter Systems Nonlinear Phenomena written by Wolfgang Desch and has been published by Birkhäuser this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.

22 papers on control of nonlinear partial differential equations highlight the area from a broad variety of viewpoints. They comprise theoretical considerations such as optimality conditions, relaxation, or stabilizability theorems, as well as the development and evaluation of new algorithms. A significant part of the volume is devoted to applications in engineering, continuum mechanics and population biology.

Robust Minimum Density Estimators And Stochastic Resonance For Classification Algorithms

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Author :
language : en
Publisher:
Release Date : 2004

Robust Minimum Density Estimators And Stochastic Resonance For Classification Algorithms written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.

The class of Robust Minimum Density Estimators (RMDEââ'¬â"¢s) are a subset of the Minimum Density Estimators (MDE). Unlike most statistical techniques, RMDEââ'¬â"¢s treat a sample as a single observation of a random distribution function. The deviance of a small number of observations does not change the general shape of the random distribution function. As the RMDE finds estimators based on the general shape of the random distribution function, the RMDE has a great resistance to outliers. Asymptotic results of the RMDE are presented including consistency and bounds on the variance function. Once the asymptotic results are presented, the generality of the estimator is presented. Techniques of parameter estimation and regression specific to the RMDE are developed. Simulations are presented to compare the RMDE estimator with standard estimation methods with and without the addition of outliers. The methods are then extended to regression problems which does not differ for linear, nonlinear regression problems or even heteroscedastic errors. Leveraging the capabilities of the RMDE is the adaptation of Bayesian analysis to create an alternative posterior distribution. By exploiting a density associated with the RMDE estimator, a posterior distribution can be created which is incredibly robust to outliers in datasets. Simulations are used to compare the regular Bayesian posterior distribution with the RMDE posterior distribution. Techniques to implement standard Bayesian methods using the RMDE posterior distribution are described. A discussion of simulating from the posterior distribution, sequential updating of the posterior, and creation of Bayesian credible regions is presented.