Download or read book Festschrift for Lucien Le Cam written by David Pollard and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contributed in honour of Lucien Le Cam on the occasion of his 70th birthday, the papers reflect the immense influence that his work has had on modern statistics. They include discussions of his seminal ideas, historical perspectives, and contributions to current research - spanning two centuries with a new translation of a paper of Daniel Bernoulli. The volume begins with a paper by Aalen, which describes Le Cams role in the founding of the martingale analysis of point processes, and ends with one by Yu, exploring the position of just one of Le Cams ideas in modern semiparametric theory. The other 27 papers touch on areas such as local asymptotic normality, contiguity, efficiency, admissibility, minimaxity, empirical process theory, and biological medical, and meteorological applications - where Le Cams insights have laid the foundations for new theories.
Download or read book Adaptive Model Selection written by Yongli Zhang and published by . This book was released on 2007 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Adaptive Learning Methods for Nonlinear System Modeling written by Danilo Comminiello and published by Butterworth-Heinemann. This book was released on 2018-06-11 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent advances on adaptive algorithms and machine learning methods designed for nonlinear system modeling and identification. Real-life problems always entail a certain degree of nonlinearity, which makes linear models a non-optimal choice. This book mainly focuses on those methodologies for nonlinear modeling that involve any adaptive learning approaches to process data coming from an unknown nonlinear system. By learning from available data, such methods aim at estimating the nonlinearity introduced by the unknown system. In particular, the methods presented in this book are based on online learning approaches, which process the data example-by-example and allow to model even complex nonlinearities, e.g., showing time-varying and dynamic behaviors. Possible fields of applications of such algorithms includes distributed sensor networks, wireless communications, channel identification, predictive maintenance, wind prediction, network security, vehicular networks, active noise control, information forensics and security, tracking control in mobile robots, power systems, and nonlinear modeling in big data, among many others. This book serves as a crucial resource for researchers, PhD and post-graduate students working in the areas of machine learning, signal processing, adaptive filtering, nonlinear control, system identification, cooperative systems, computational intelligence. This book may be also of interest to the industry market and practitioners working with a wide variety of nonlinear systems. - Presents the key trends and future perspectives in the field of nonlinear signal processing and adaptive learning. - Introduces novel solutions and improvements over the state-of-the-art methods in the very exciting area of online and adaptive nonlinear identification. - Helps readers understand important methods that are effective in nonlinear system modelling, suggesting the right methodology to address particular issues.
Download or read book Principles and Theory for Data Mining and Machine Learning written by Bertrand Clarke and published by Springer Science & Business Media. This book was released on 2009-07-21 with total page 786 pages. Available in PDF, EPUB and Kindle. Book excerpt: Extensive treatment of the most up-to-date topics Provides the theory and concepts behind popular and emerging methods Range of topics drawn from Statistics, Computer Science, and Electrical Engineering
Download or read book Adaptive Regression for Modeling Nonlinear Relationships written by George J. Knafl and published by Springer. This book was released on 2016-09-20 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables into regression models for different types of outcome variables. Such nonlinear dependence is often not considered in applied research, yet nonlinear relationships are common and so need to be addressed. A standard linear analysis can produce misleading conclusions, while a nonlinear analysis can provide novel insights into data, not otherwise possible. A variety of examples of the benefits of modeling nonlinear relationships are presented throughout the book. Methods are covered using what are called fractional polynomials based on real-valued power transformations of primary predictor variables combined with model selection based on likelihood cross-validation. The book covers how to formulate and conduct such adaptive fractional polynomial modeling in the standard, logistic, and Poisson regression contexts with continuous, discrete, and counts outcomes, respectively, either univariate or multivariate. The book also provides a comparison of adaptive modeling to generalized additive modeling (GAM) and multiple adaptive regression splines (MARS) for univariate outcomes. The authors have created customized SAS macros for use in conducting adaptive regression modeling. These macros and code for conducting the analyses discussed in the book are available through the first author's website and online via the book’s Springer website. Detailed descriptions of how to use these macros and interpret their output appear throughout the book. These methods can be implemented using other programs.
Download or read book Adaptive Modelling Estimation and Fusion from Data written by Chris Harris and published by Springer Science & Business Media. This book was released on 2012-10-05 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together for the first time the complete theory of data based neurofuzzy modelling and the linguistic attributes of fuzzy logic in a single cohesive mathematical framework. After introducing the basic theory of data based modelling new concepts including extended additive and multiplicative submodels are developed. All of these algorithms are illustrated with benchmark examples to demonstrate their efficiency. The book aims at researchers and advanced professionals in time series modelling, empirical data modelling, knowledge discovery, data mining and data fusion.
Download or read book Concentration Inequalities and Model Selection written by Pascal Massart and published by Springer. This book was released on 2007-04-26 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: Concentration inequalities have been recognized as fundamental tools in several domains such as geometry of Banach spaces or random combinatorics. They also turn to be essential tools to develop a non asymptotic theory in statistics. This volume provides an overview of a non asymptotic theory for model selection. It also discusses some selected applications to variable selection, change points detection and statistical learning.
Download or read book Model Selection written by Parhasarathi Lahiri and published by IMS. This book was released on 2001 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Adaptive Decision Maker written by John W. Payne and published by Cambridge University Press. This book was released on 1993-05-28 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Adaptive Decision Maker argues that people use a variety of strategies to make judgments and choices. The authors introduce a model that shows how decision makers balance effort and accuracy considerations and predicts which strategy a person will use in a given situation. A series of experiments testing the model are presented, and the authors analyse how the model can lead to improved decisions and opportunities for further research.
Download or read book Machine Learning in Non Stationary Environments written by Masashi Sugiyama and published by MIT Press. This book was released on 2012-03-30 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theory, algorithms, and applications of machine learning techniques to overcome “covariate shift” non-stationarity. As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.
Download or read book The Roy Adaptation Model written by Callista Roy and published by Prentice Hall. This book was released on 1999 with total page 604 pages. Available in PDF, EPUB and Kindle. Book excerpt: Compact and consistent, this book focuses on the essentials of nursing practice and theory while integrating the conceptual framework of the Model into contemporary practice. Standardized nursing NANDA diagnoses are used consistently throughout the book.
Download or read book Computational Interaction written by Antti Oulasvirta and published by Oxford University Press. This book was released on 2018 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces a new perspective on how to design user interfaces called "Computational Interaction". This new method applies principles of computational thinking (abstraction, automation and analysis) to inform our understanding of how people interact with user interfaces.
Download or read book Software Engineering for Self Adaptive Systems written by Betty H. C. Cheng and published by Springer Science & Business Media. This book was released on 2009-06-19 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: The carefully reviewed papers in this state-of-the-art survey describe a wide range of approaches coming from different strands of software engineering, and look forward to future challenges facing this ever-resurgent and exacting field of research.
Download or read book Learning Based Adaptive Control written by Mouhacine Benosman and published by Butterworth-Heinemann. This book was released on 2016-08-02 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive control has been one of the main problems studied in control theory. The subject is well understood, yet it has a very active research frontier. This book focuses on a specific subclass of adaptive control, namely, learning-based adaptive control. As systems evolve during time or are exposed to unstructured environments, it is expected that some of their characteristics may change. This book offers a new perspective about how to deal with these variations. By merging together Model-Free and Model-Based learning algorithms, the author demonstrates, using a number of mechatronic examples, how the learning process can be shortened and optimal control performance can be reached and maintained. - Includes a good number of Mechatronics Examples of the techniques. - Compares and blends Model-free and Model-based learning algorithms. - Covers fundamental concepts, state-of-the-art research, necessary tools for modeling, and control.
Download or read book The Minimum Description Length Principle written by Peter D. Grünwald and published by MIT Press. This book was released on 2007 with total page 736 pages. Available in PDF, EPUB and Kindle. Book excerpt: This introduction to the MDL Principle provides a reference accessible to graduate students and researchers in statistics, pattern classification, machine learning, and data mining, to philosophers interested in the foundations of statistics, and to researchers in other applied sciences that involve model selection.
Download or read book User Modeling Adaptation and Personalization written by Geert-Jan Houben and published by Springer Science & Business Media. This book was released on 2009-06-08 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the First International Conference on User Modeling, Adaptation, and Personalization, held in Trento, Italy, on June 22-26, 2009. This annual conference was merged from the biennial conference series User Modeling, UM, and the conference on Adaptive Hypermedia and Adaptive Web-Based Systems, AH. The 53 papers presented together with 3 invited talks were carefully reviewed and selected from 125 submissions. The tutorials and workshops were organized in topical sections on constraint-based tutoring systems; new paradigms for adaptive interaction; adaption and personalization for Web 2.0; lifelong user modelling; personalization in mobile and pervasive computing; ubiquitous user modeling; user-centred design and evaluation of adaptive systems.
Download or read book Adaptive Regression written by Yadolah Dodge and published by Springer Science & Business Media. This book was released on 2012-10-01 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: While there have been a large number of estimation methods proposed and developed for linear regression, none has proved good for all purposes. This text focuses on the construction of an adaptive combination of two estimation methods so as to help users make an objective choice and combine the desirable properties of two estimators.
