Nonlinear Estimation and Classification

Nonlinear Estimation and Classification PDF Author: David D. Denison
Publisher: Springer Science & Business Media
ISBN: 0387215794
Category : Mathematics
Languages : en
Pages : 465

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Book Description
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.

Nonlinear Estimation and Classification

Nonlinear Estimation and Classification PDF Author: David D. Denison
Publisher: Springer Science & Business Media
ISBN: 0387215794
Category : Mathematics
Languages : en
Pages : 465

Get Book Here

Book Description
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.

Nonlinear Modeling

Nonlinear Modeling PDF Author: Johan A. K. Suykens
Publisher: Springer Science & Business Media
ISBN: 9780792381952
Category : Language Arts & Disciplines
Languages : en
Pages : 284

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Book Description
This collection of eight contributions presents advanced black-box techniques for nonlinear modeling. The methods discussed include neural nets and related model structures for nonlinear system identification, enhanced multi-stream Kalman filter training for recurrent networks, the support vector method of function estimation, parametric density estimation for the classification of acoustic feature vectors in speech recognition, wavelet based modeling of nonlinear systems, nonlinear identification based on fuzzy models, statistical learning in control and matrix theory, and nonlinear time- series analysis. The volume concludes with the results of a time- series prediction competition held at a July 1998 workshop in Belgium. Annotation copyrighted by Book News, Inc., Portland, OR.

Decision Forests

Decision Forests PDF Author: Antonio Criminisi
Publisher: Foundations and Trends(r) in C
ISBN: 9781601985408
Category : Computers
Languages : en
Pages : 162

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Book Description
Presents a unified, efficient model of random decision forests which can be used in a number of applications such as scene recognition from photographs, object recognition in images, automatic diagnosis from radiological scans and document analysis.

Classification, Parameter Estimation and State Estimation

Classification, Parameter Estimation and State Estimation PDF Author: Ferdinand van der Heijden
Publisher: John Wiley & Sons
ISBN: 0470090146
Category : Science
Languages : en
Pages : 440

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Book Description
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

Nonlinear Approaches in Engineering Applications

Nonlinear Approaches in Engineering Applications PDF Author: Liming Dai
Publisher: Springer
ISBN: 3319694804
Category : Technology & Engineering
Languages : en
Pages : 472

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Book Description
This book analyzes the updated principles and applications of nonlinear approaches to solve engineering and physics problems. The knowledge on nonlinearity and the comprehension of nonlinear approaches are inevitable to future engineers and scientists, making this an ideal book for engineers, engineering students, and researchers in engineering, physics, and mathematics. Chapters are of specific interest to readers who seek expertise in optimization, nonlinear analysis, mathematical modeling of complex forms, and non-classical engineering problems. The book covers methodologies and applications from diverse areas such as vehicle dynamics, surgery simulation, path planning, mobile robots, contact and scratch analysis at the micro and nano scale, sub-structuring techniques, ballistic projectiles, and many more.

Lectures on Systems, Control, and Information

Lectures on Systems, Control, and Information PDF Author: Lei Guo
Publisher: American Mathematical Soc.
ISBN: 0821820095
Category : Mathematics
Languages : en
Pages : 226

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Book Description
This volume presents lectures delivered at a workshop held at the Chinese Academy of Sciences (Bejing). The following articles are included: "Nonlinear Systems Control" by R. Brockett, "Adaptive Control of Discrete-Time Nonlinear Systems with Structural Uncertainties" by L.-L. Xie and L. Guo, "Networks and Learning" by P. R. Kumar, "Mathematical Aspects of the Power Control Problem in Mobile Communication Systems" by C. W. Sung and W. S. Wong, and "Brockett's Problem on Nonlinear Filtering Theory" by S. S.-T. Yau. Basic concepts and current research are both presented in this book. The volume offers a comprehensive and easy-to-follow account of many fundamental issues in this diverse field. It would be a suitable text for a graduate course on wireless communication. Titles in this series are co-published with International Press, Cambridge, MA.

Encyclopedia of Healthcare Information Systems

Encyclopedia of Healthcare Information Systems PDF Author: Wickramasinghe, Nilmini
Publisher: IGI Global
ISBN: 1599048906
Category : Computers
Languages : en
Pages : 1465

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Book Description
Healthcare, a vital industry that touches most of us in our lives, faces major challenges in demographics, technology, and finance. Longer life expectancy and an aging population, technological advancements that keep people younger and healthier, and financial issues area constant strain on healthcare organizations' resources and management. Focusing on the organization's ability to improve access, quality, and value of care to the patient may present possible solutions to these challenges. The Encyclopedia of Healthcare Information Systems provides an extensive and rich compilation of international research, discussing the use, adoption, design, and diffusion of information communication technologies (ICTs) in healthcare, including the role of ICTs in the future of healthcare delivery; access, quality, and value of healthcare; nature and evaluation of medical technologies; ethics and social implications; and medical information management.

Foundations of Computational Mathematics

Foundations of Computational Mathematics PDF Author: Ronald A. DeVore
Publisher: Cambridge University Press
ISBN: 9780521003490
Category : Mathematics
Languages : en
Pages : 418

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Book Description
Collection of papers by leading researchers in computational mathematics, suitable for graduate students and researchers.

A Distribution-Free Theory of Nonparametric Regression

A Distribution-Free Theory of Nonparametric Regression PDF Author: László Györfi
Publisher: Springer Science & Business Media
ISBN: 0387224424
Category : Mathematics
Languages : en
Pages : 662

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Book Description
This book provides a systematic in-depth analysis of nonparametric regression with random design. It covers almost all known estimates. The emphasis is on distribution-free properties of the estimates.

Statistical Regression and Classification

Statistical Regression and Classification PDF Author: Norman Matloff
Publisher: CRC Press
ISBN: 1351645897
Category : Business & Economics
Languages : en
Pages : 439

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Book Description
Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. The text takes a modern look at regression: * A thorough treatment of classical linear and generalized linear models, supplemented with introductory material on machine learning methods. * Since classification is the focus of many contemporary applications, the book covers this topic in detail, especially the multiclass case. * In view of the voluminous nature of many modern datasets, there is a chapter on Big Data. * Has special Mathematical and Computational Complements sections at ends of chapters, and exercises are partitioned into Data, Math and Complements problems. * Instructors can tailor coverage for specific audiences such as majors in Statistics, Computer Science, or Economics. * More than 75 examples using real data. The book treats classical regression methods in an innovative, contemporary manner. Though some statistical learning methods are introduced, the primary methodology used is linear and generalized linear parametric models, covering both the Description and Prediction goals of regression methods. The author is just as interested in Description applications of regression, such as measuring the gender wage gap in Silicon Valley, as in forecasting tomorrow's demand for bike rentals. An entire chapter is devoted to measuring such effects, including discussion of Simpson's Paradox, multiple inference, and causation issues. Similarly, there is an entire chapter of parametric model fit, making use of both residual analysis and assessment via nonparametric analysis. Norman Matloff is a professor of computer science at the University of California, Davis, and was a founder of the Statistics Department at that institution. His current research focus is on recommender systems, and applications of regression methods to small area estimation and bias reduction in observational studies. He is on the editorial boards of the Journal of Statistical Computation and the R Journal. An award-winning teacher, he is the author of The Art of R Programming and Parallel Computation in Data Science: With Examples in R, C++ and CUDA.