An Elementary Introduction To Statistical Learning Theory

An Elementary Introduction to Statistical Learning Theory PDF
Author: Sanjeev Kulkarni
Publisher: John Wiley & Sons
ISBN: 9781118023464
Size: 80.73 MB
Format: PDF
Category : Mathematics
Languages : en
Pages : 288
View: 3005

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An Elementary Introduction To Statistical Learning Theory

by Sanjeev Kulkarni, An Elementary Introduction To Statistical Learning Theory Books available in PDF, EPUB, Mobi Format. Download An Elementary Introduction To Statistical Learning Theory books, A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors present the basic theory behind contemporary machine learning and uniquely utilize its foundations as a framework for philosophical thinking about inductive inference. Promoting the fundamental goal of statistical learning, knowing what is achievable and what is not, this book demonstrates the value of a systematic methodology when used along with the needed techniques for evaluating the performance of a learning system. First, an introduction to machine learning is presented that includes brief discussions of applications such as image recognition, speech recognition, medical diagnostics, and statistical arbitrage. To enhance accessibility, two chapters on relevant aspects of probability theory are provided. Subsequent chapters feature coverage of topics such as the pattern recognition problem, optimal Bayes decision rule, the nearest neighbor rule, kernel rules, neural networks, support vector machines, and boosting. Appendices throughout the book explore the relationship between the discussed material and related topics from mathematics, philosophy, psychology, and statistics, drawing insightful connections between problems in these areas and statistical learning theory. All chapters conclude with a summary section, a set of practice questions, and a reference sections that supplies historical notes and additional resources for further study. An Elementary Introduction to Statistical Learning Theory is an excellent book for courses on statistical learning theory, pattern recognition, and machine learning at the upper-undergraduate and graduate levels. It also serves as an introductory reference for researchers and practitioners in the fields of engineering, computer science, philosophy, and cognitive science that would like to further their knowledge of the topic.


An Elementary Introduction To Statistical Learning Theory

An Elementary Introduction to Statistical Learning Theory PDF
Author: Sanjeev Kulkarni
Publisher: John Wiley & Sons
ISBN: 9780470641835
Size: 38.94 MB
Format: PDF, Docs
Category : Mathematics
Languages : en
Pages : 209
View: 2099

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An Elementary Introduction To Statistical Learning Theory

by Sanjeev Kulkarni, An Elementary Introduction To Statistical Learning Theory Books available in PDF, EPUB, Mobi Format. Download An Elementary Introduction To Statistical Learning Theory books, "A joint endeavor from leading researchers in the fields of philosophy and electrical engineering An Introduction to Statistical Learning Theory provides a broad and accessible introduction to rapidly evolving field of statistical pattern recognition andstatistical learning theory. Exploring topics that are not often covered in introductory level books on statistical learning theory, including PAC learning, VC dimension, and simplicity, the authors present upper-undergraduate and graduate levels with the basic theory behind contemporary machine learning and uniquely suggest it serves as an excellent framework for philosophical thinking about inductive inference"--Back cover.


An Elementary Introduction To Statistical Learning Theory

An Elementary Introduction to Statistical Learning Theory PDF
Author: Sanjeev Kulkarni
Publisher: John Wiley & Sons
ISBN: 9780470641835
Size: 63.66 MB
Format: PDF, Docs
Category : Mathematics
Languages : en
Pages : 209
View: 853

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An Elementary Introduction To Statistical Learning Theory

by Sanjeev Kulkarni, An Elementary Introduction To Statistical Learning Theory Books available in PDF, EPUB, Mobi Format. Download An Elementary Introduction To Statistical Learning Theory books, "A joint endeavor from leading researchers in the fields of philosophy and electrical engineering An Introduction to Statistical Learning Theory provides a broad and accessible introduction to rapidly evolving field of statistical pattern recognition andstatistical learning theory. Exploring topics that are not often covered in introductory level books on statistical learning theory, including PAC learning, VC dimension, and simplicity, the authors present upper-undergraduate and graduate levels with the basic theory behind contemporary machine learning and uniquely suggest it serves as an excellent framework for philosophical thinking about inductive inference"--Back cover.


Mixed Models

Mixed Models PDF
Author: Eugene Demidenko
Publisher: John Wiley & Sons
ISBN: 1118592999
Size: 27.57 MB
Format: PDF, ePub, Mobi
Category : Mathematics
Languages : en
Pages : 754
View: 5644

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Mixed Models

by Eugene Demidenko, Mixed Models Books available in PDF, EPUB, Mobi Format. Download Mixed Models books, Praise for the First Edition “This book will serve to greatly complement the growing number of texts dealing with mixed models, and I highly recommend including it in one’s personal library.” —Journal of the American Statistical Association Mixed modeling is a crucial area of statistics, enabling the analysis of clustered and longitudinal data. Mixed Models: Theory and Applications with R, Second Edition fills a gap in existing literature between mathematical and applied statistical books by presenting a powerful examination of mixed model theory and application with special attention given to the implementation in R. The new edition provides in-depth mathematical coverage of mixed models’ statistical properties and numerical algorithms, as well as nontraditional applications, such as regrowth curves, shapes, and images. The book features the latest topics in statistics including modeling of complex clustered or longitudinal data, modeling data with multiple sources of variation, modeling biological variety and heterogeneity, Healthy Akaike Information Criterion (HAIC), parameter multidimensionality, and statistics of image processing. Mixed Models: Theory and Applications with R, Second Edition features unique applications of mixed model methodology, as well as: Comprehensive theoretical discussions illustrated by examples and figures Over 300 exercises, end-of-section problems, updated data sets, and R subroutines Problems and extended projects requiring simulations in R intended to reinforce material Summaries of major results and general points of discussion at the end of each chapter Open problems in mixed modeling methodology, which can be used as the basis for research or PhD dissertations Ideal for graduate-level courses in mixed statistical modeling, the book is also an excellent reference for professionals in a range of fields, including cancer research, computer science, and engineering.


The Bloomsbury Encyclopedia Of Philosophers In America

The Bloomsbury Encyclopedia of Philosophers in America PDF
Author: John R. Shook
Publisher: Bloomsbury Publishing
ISBN: 1472570561
Size: 65.99 MB
Format: PDF, ePub, Mobi
Category : Philosophy
Languages : en
Pages : 944
View: 6340

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The Bloomsbury Encyclopedia Of Philosophers In America

by John R. Shook, The Bloomsbury Encyclopedia Of Philosophers In America Books available in PDF, EPUB, Mobi Format. Download The Bloomsbury Encyclopedia Of Philosophers In America books, For scholars working on almost any aspect of American thought, The Bloomsbury Encyclopedia to Philosophers in America presents an indispensable reference work. Selecting over 700 figures from the Dictionary of Early American Philosophers and the Dictionary of Modern American Philosophers, this condensed edition includes key contributors to philosophical thought. From 1600 to the present day, entries cover psychology, pedagogy, sociology, anthropology, education, theology and political science, before these disciplines came to be considered distinct from philosophy. Clear and accessible, each entry contains a short biography of the writer, an exposition and analysis of his or her doctrines and ideas, a bibliography of writings and suggestions for further reading. Featuring a new preface by the editor and a comprehensive introduction, The Bloomsbury Encyclopedia to Philosophers in America includes 30 new entries on twenty-first century thinkers including Martha Nussbaum and Patricia Churchland. With in-depth overviews of Waldo Emerson, Margaret Fuller, Noah Porter, Frederick Rauch, Benjamin Franklin, Thomas Paine and Thomas Jefferson, this is an invaluable one-stop research volume to understanding leading figures in American thought and the development of American intellectual history.


Using The Weibull Distribution

Using the Weibull Distribution PDF
Author: John I. McCool
Publisher: John Wiley & Sons
ISBN: 1118351983
Size: 50.56 MB
Format: PDF, ePub, Mobi
Category : Technology & Engineering
Languages : en
Pages : 368
View: 3846

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Using The Weibull Distribution

by John I. McCool, Using The Weibull Distribution Books available in PDF, EPUB, Mobi Format. Download Using The Weibull Distribution books, Understand and utilize the latest developments in Weibull inferential methods While the Weibull distribution is widely used in science and engineering, most engineers do not have the necessary statistical training to implement the methodology effectively. Using the Weibull Distribution: Reliability, Modeling, and Inference fills a gap in the current literature on the topic, introducing a self-contained presentation of the probabilistic basis for the methodology while providing powerful techniques for extracting information from data. The author explains the use of the Weibull distribution and its statistical and probabilistic basis, providing a wealth of material that is not available in the current literature. The book begins by outlining the fundamental probability and statistical concepts that serve as a foundation for subsequent topics of coverage, including: • Optimum burn-in, age and block replacement, warranties and renewal theory • Exact inference in Weibull regression • Goodness of fit testing and distinguishing the Weibull from the lognormal • Inference for the Three Parameter Weibull Throughout the book, a wealth of real-world examples showcases the discussed topics and each chapter concludes with a set of exercises, allowing readers to test their understanding of the presented material. In addition, a related website features the author's own software for implementing the discussed analyses along with a set of modules written in Mathcad®, and additional graphical interface software for performing simulations. With its numerous hands-on examples, exercises, and software applications, Using the Weibull Distribution is an excellent book for courses on quality control and reliability engineering at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for engineers, scientists, and business analysts who gather and interpret data that follows the Weibull distribution


The Analysis Of Covariance And Alternatives

The Analysis of Covariance and Alternatives PDF
Author: Bradley Huitema
Publisher: John Wiley & Sons
ISBN: 9781118067468
Size: 63.86 MB
Format: PDF, ePub, Docs
Category : Mathematics
Languages : en
Pages : 480
View: 2670

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The Analysis Of Covariance And Alternatives

by Bradley Huitema, The Analysis Of Covariance And Alternatives Books available in PDF, EPUB, Mobi Format. Download The Analysis Of Covariance And Alternatives books, A complete guide to cutting-edge techniques and best practices for applying covariance analysis methods The Second Edition of Analysis of Covariance and Alternatives sheds new light on its topic, offering in-depth discussions of underlying assumptions, comprehensive interpretations of results, and comparisons of distinct approaches. The book has been extensively revised and updated to feature an in-depth review of prerequisites and the latest developments in the field. The author begins with a discussion of essential topics relating to experimental design and analysis, including analysis of variance, multiple regression, effect size measures and newly developed methods of communicating statistical results. Subsequent chapters feature newly added methods for the analysis of experiments with ordered treatments, including two parametric and nonparametric monotone analyses as well as approaches based on the robust general linear model and reversed ordinal logistic regression. Four groundbreaking chapters on single-case designs introduce powerful new analyses for simple and complex single-case experiments. This Second Edition also features coverage of advanced methods including: Simple and multiple analysis of covariance using both the Fisher approach and the general linear model approach Methods to manage assumption departures, including heterogeneous slopes, nonlinear functions, dichotomous dependent variables, and covariates affected by treatments Power analysis and the application of covariance analysis to randomized-block designs, two-factor designs, pre- and post-test designs, and multiple dependent variable designs Measurement error correction and propensity score methods developed for quasi-experiments, observational studies, and uncontrolled clinical trials Thoroughly updated to reflect the growing nature of the field, Analysis of Covariance and Alternatives is a suitable book for behavioral and medical scineces courses on design of experiments and regression and the upper-undergraduate and graduate levels. It also serves as an authoritative reference work for researchers and academics in the fields of medicine, clinical trials, epidemiology, public health, sociology, and engineering.


Quantile Regression

Quantile Regression PDF
Author: Cristina Davino
Publisher: John Wiley & Sons
ISBN: 1118752716
Size: 36.37 MB
Format: PDF, Docs
Category : Mathematics
Languages : en
Pages : 288
View: 7186

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Quantile Regression

by Cristina Davino, Quantile Regression Books available in PDF, EPUB, Mobi Format. Download Quantile Regression books, A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensive description of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and followed by applications using real data. Quantile Regression: Presents a complete treatment of quantile regression methods, including, estimation, inference issues and application of methods. Delivers a balance between methodolgy and application Offers an overview of the recent developments in the quantile regression framework and why to use quantile regression in a variety of areas such as economics, finance and computing. Features a supporting website (www.wiley.com/go/quantile_regression) hosting datasets along with R, Stata and SAS software code. Researchers and PhD students in the field of statistics, economics, econometrics, social and environmental science and chemistry will benefit from this book.


Decision Sciences

Decision Sciences PDF
Author: Raghu Nandan Sengupta
Publisher: CRC Press
ISBN: 1351727400
Size: 57.57 MB
Format: PDF, Kindle
Category : Business & Economics
Languages : en
Pages : 1026
View: 2572

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Decision Sciences

by Raghu Nandan Sengupta, Decision Sciences Books available in PDF, EPUB, Mobi Format. Download Decision Sciences books, This handbook is an endeavour to cover many current, relevant, and essential topics related to decision sciences in a scientific manner. Using this handbook, graduate students, researchers, as well as practitioners from engineering, statistics, sociology, economics, etc. will find a new and refreshing paradigm shift as to how these topics can be put to use beneficially. Starting from the basics to advanced concepts, authors hope to make the readers well aware of the different theoretical and practical ideas, which are the focus of study in decision sciences nowadays. It includes an excellent bibliography/reference/journal list, information about a variety of datasets, illustrated pseudo-codes, and discussion of future trends in research. Covering topics ranging from optimization, networks and games, multi-objective optimization, inventory theory, statistical methods, artificial neural networks, times series analysis, simulation modeling, decision support system, data envelopment analysis, queueing theory, etc., this reference book is an attempt to make this area more meaningful for varied readers. Noteworthy features of this handbook are in-depth coverage of different topics, solved practical examples, unique datasets for a variety of examples in the areas of decision sciences, in-depth analysis of problems through colored charts, 3D diagrams, and discussions about software.


A Guided Tour Of Artificial Intelligence Research

A Guided Tour of Artificial Intelligence Research PDF
Author: Pierre Marquis
Publisher: Springer Nature
ISBN: 3030061647
Size: 49.89 MB
Format: PDF, ePub, Docs
Category : Technology & Engineering
Languages : en
Pages : 803
View: 2602

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A Guided Tour Of Artificial Intelligence Research

by Pierre Marquis, A Guided Tour Of Artificial Intelligence Research Books available in PDF, EPUB, Mobi Format. Download A Guided Tour Of Artificial Intelligence Research books, The purpose of this book is to provide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues. It is aimed at an audience of master students and Ph.D. students, and can be of interest as well for researchers and engineers who want to know more about AI. The book is split into three volumes: - the first volume brings together twenty-three chapters dealing with the foundations of knowledge representation and the formalization of reasoning and learning (Volume 1. Knowledge representation, reasoning and learning) - the second volume offers a view of AI, in fourteen chapters, from the side of the algorithms (Volume 2. AI Algorithms) - the third volume, composed of sixteen chapters, describes the main interfaces and applications of AI (Volume 3. Interfaces and applications of AI). Implementing reasoning or decision making processes requires an appropriate representation of the pieces of information to be exploited. This first volume starts with a historical chapter sketching the slow emergence of building blocks of AI along centuries. Then the volume provides an organized overview of different logical, numerical, or graphical representation formalisms able to handle incomplete information, rules having exceptions, probabilistic and possibilistic uncertainty (and beyond), as well as taxonomies, time, space, preferences, norms, causality, and even trust and emotions among agents. Different types of reasoning, beyond classical deduction, are surveyed including nonmonotonic reasoning, belief revision, updating, information fusion, reasoning based on similarity (case-based, interpolative, or analogical), as well as reasoning about actions, reasoning about ontologies (description logics), argumentation, and negotiation or persuasion between agents. Three chapters deal with decision making, be it multiple criteria, collective, or under uncertainty. Two chapters cover statistical computational learning and reinforcement learning (other machine learning topics are covered in Volume 2). Chapters on diagnosis and supervision, validation and explanation, and knowledge base acquisition complete the volume.