*Concepts, Theory, and Methods*

Author: Vladimir Cherkassky,Filip M. Mulier

Publisher: John Wiley & Sons

ISBN: 9780470140512

Category: Computers

Page: 624

View: 8154

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Search Results for: learning-from-data

## Learning from Data

An interdisciplinary framework for learning methodologies—covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied—showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.
## Learning from Data

Ten years ago Bill Gale of AT&T Bell Laboratories was primary organizer of the first Workshop on Artificial Intelligence and Statistics. In the early days of the Workshop series it seemed clear that researchers in AI and statistics had common interests, though with different emphases, goals, and vocabularies. In learning and model selection, for example, a historical goal of AI to build autonomous agents probably contributed to a focus on parameter-free learning systems, which relied little on an external analyst's assumptions about the data. This seemed at odds with statistical strategy, which stemmed from a view that model selection methods were tools to augment, not replace, the abilities of a human analyst. Thus, statisticians have traditionally spent considerably more time exploiting prior information of the environment to model data and exploratory data analysis methods tailored to their assumptions. In statistics, special emphasis is placed on model checking, making extensive use of residual analysis, because all models are 'wrong', but some are better than others. It is increasingly recognized that AI researchers and/or AI programs can exploit the same kind of statistical strategies to good effect. Often AI researchers and statisticians emphasized different aspects of what in retrospect we might now regard as the same overriding tasks.
## The Health Care Data Guide

The Health Care Data Guide is designed to help students and professionals build a skill set specific to using data for improvement of health care processes and systems. Even experienced data users will find valuable resources among the tools and cases that enrich The Health Care Data Guide. Practical and step-by-step, this book spotlights statistical process control (SPC) and develops a philosophy, a strategy, and a set of methods for ongoing improvement to yield better outcomes. Provost and Murray reveal how to put SPC into practice for a wide range of applications including evaluating current process performance, searching for ideas for and determining evidence of improvement, and tracking and documenting sustainability of improvement. A comprehensive overview of graphical methods in SPC includes Shewhart charts, run charts, frequency plots, Pareto analysis, and scatter diagrams. Other topics include stratification and rational sub-grouping of data and methods to help predict performance of processes. Illustrative examples and case studies encourage users to evaluate their knowledge and skills interactively and provide opportunity to develop additional skills and confidence in displaying and interpreting data. Companion Web site: www.josseybass.com/go/provost
## Statistics: Learning from Data

STATISTICS: LEARNING FROM DATA, Second Edition, addresses common problems faced by learners of elementary statistics with an innovative approach. The authors have paid particular attention to areas learners often struggle with -- probability, hypothesis testing, and selecting an appropriate method of analysis. Probability coverage is based on current research on how students best learn the subject. A unique chapter that provides an informal introduction to the ideas of statistical inference helps students to develop a framework for choosing an appropriate method. Supported by learning objectives, real-data examples and exercises, and technology notes, this book helps learners to develop conceptual understanding, mechanical proficiency, and the ability to put knowledge into practice. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
## Utility-Based Learning from Data

Utility-Based Learning from Data provides a pedagogical, self-contained discussion of probability estimation methods via a coherent approach from the viewpoint of a decision maker who acts in an uncertain environment. This approach is motivated by the idea that probabilistic models are usually not learned for their own sake; rather, they are used to make decisions. Specifically, the authors adopt the point of view of a decision maker who (i) operates in an uncertain environment where the consequences of every possible outcome are explicitly monetized, (ii) bases his decisions on a probabilistic model, and (iii) builds and assesses his models accordingly. These assumptions are naturally expressed in the language of utility theory, which is well known from finance and decision theory. By taking this point of view, the book sheds light on and generalizes some popular statistical learning approaches, connecting ideas from information theory, statistics, and finance. It strikes a balance between rigor and intuition, conveying the main ideas to as wide an audience as possible.
## Advanced Analytics with Spark

In the second edition of this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. Updated for Spark 2.1, this edition acts as an introduction to these techniques and other best practices in Spark programming. You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—including classification, clustering, collaborative filtering, and anomaly detection—to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you’ll find the book’s patterns useful for working on your own data applications. With this book, you will: Familiarize yourself with the Spark programming model Become comfortable within the Spark ecosystem Learn general approaches in data science Examine complete implementations that analyze large public data sets Discover which machine learning tools make sense for particular problems Acquire code that can be adapted to many uses
## Learning from Data Streams

Processing data streams has raised new research challenges over the last few years. This book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system. Applications in security, the natural sciences, and education are presented. The huge bibliography offers an excellent starting point for further reading and future research.
## Scientific Inference

Providing the knowledge and practical experience to begin analysing scientific data, this book is ideal for physical sciences students wishing to improve their data handling skills. The book focuses on explaining and developing the practice and understanding of basic statistical analysis, concentrating on a few core ideas, such as the visual display of information, modelling using the likelihood function, and simulating random data. Key concepts are developed through a combination of graphical explanations, worked examples, example computer code and case studies using real data. Students will develop an understanding of the ideas behind statistical methods and gain experience in applying them in practice. Further resources are available at www.cambridge.org/9781107607590, including data files for the case studies so students can practise analysing data, and exercises to test students' understanding.
## Statistics

Written as a study tool, the Lab Workbook is keyed directly to the text to provide section by section review and practice for the first ten chapters of Agresti/Franklin 2/e. Print outs of the activities found on the Student CD are included in the Lab Workbook.
## Learning From Data

Learning from Data focuses on how to interpret psychological data and statistical results. The authors review the basics of statistical reasoning to helpstudents better understand relevant data that affecttheir everyday lives. Numerous examples based on current research and events are featured throughout.To facilitate learning, authors Glenberg and Andrzejewski: Devote extra attention to explaining the more difficult concepts and the logic behind them Use repetition to enhance students’ memories with multiple examples, reintroductions of the major concepts, and a focus on these concepts in the problems Employ a six-step procedure for describing all statistical tests from the simplest to the most complex Provide end-of-chapter tables to summarize the hypothesis testing procedures introduced Emphasizes how to choose the best procedure in the examples, problems and endpapers Focus on power with a separate chapter and power analyses procedures in each chapter Provide detailed explanations of factorial designs, interactions, and ANOVA to help students understand the statistics used in professional journal articles. The third edition has a user-friendly approach: Designed to be used seamlessly with Excel, all of the in-text analyses are conducted in Excel, while the book’s CD contains files for conducting analyses in Excel, as well as text files that can be analyzed in SPSS, SAS, and Systat Two large, real data sets integrated throughout illustrate important concepts Many new end-of-chapter problems (definitions, computational, and reasoning) and many more on the companion CD Online Instructor’s Resources includes answers to all the exercises in the book and multiple-choice test questions with answers Boxed media reports illustrate key concepts and their relevance to realworld issues The inclusion of effect size in all discussions of power accurately reflects the contemporary issues of power, effect size, and significance. Learning From Data, Third Edition is intended as a text for undergraduate or beginning graduate statistics courses in psychology, education, and other applied social and health sciences.
## Learning from Data Streams in Dynamic Environments

This book addresses the problems of modeling, prediction, classification, data understanding and processing in non-stationary and unpredictable environments. It presents major and well-known methods and approaches for the design of systems able to learn and to fully adapt its structure and to adjust its parameters according to the changes in their environments. Also presents the problem of learning in non-stationary environments, its interests, its applications and challenges and studies the complementarities and the links between the different methods and techniques of learning in evolving and non-stationary environments.
## Learning from data

This manual supplements Learning from Data: An Introduction to Statistical Reasoning. The chapter organization of the manual corresponds to that of the textbook. Each chapter of the manual is in two parts. The first part contains answers to the end-of-chapter exercises in the textbook. The second part contains multiple-choice questions from which instructors can make up tests.
## Learning From Data

Facts101 is your complete guide to Learning From Data. In this book, you will learn topics such as as those in your book plus much more. With key features such as key terms, people and places, Facts101 gives you all the information you need to prepare for your next exam. Our practice tests are specific to the textbook and we have designed tools to make the most of your limited study time.
## Learning's from Data Storage

## Learning from Data , An Introduction to Statistical Reasoning

Facts101 is your complete guide to Learning from Data , An Introduction to Statistical Reasoning. In this book, you will learn topics such as as those in your book plus much more. With key features such as key terms, people and places, Facts101 gives you all the information you need to prepare for your next exam. Our practice tests are specific to the textbook and we have designed tools to make the most of your limited study time.
## Statistics

## Statistics, The Art and Science of Learning from Data

Facts101 is your complete guide to Statistics, The Art and Science of Learning from Data. In this book, you will learn topics such as as those in your book plus much more. With key features such as key terms, people and places, Facts101 gives you all the information you need to prepare for your next exam. Our practice tests are specific to the textbook and we have designed tools to make the most of your limited study time.
## Hands-On Machine Learning with Scikit-Learn and TensorFlow

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
## Statistics

Contains detailed tutorial instructions and worked out examples & exercises for TI-83+/84 Series Calculator, Minitab(R), JMP(R), StatCrunch, SPSS(R), and Excel(R) (including PHStat, an Excel plug-in).

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*Concepts, Theory, and Methods*

Author: Vladimir Cherkassky,Filip M. Mulier

Publisher: John Wiley & Sons

ISBN: 9780470140512

Category: Computers

Page: 624

View: 8154

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Publisher: Springer Science & Business Media

ISBN: 1461224047

Category: Mathematics

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Author: Lloyd P. Provost,Sandra Murray

Publisher: John Wiley & Sons

ISBN: 0470902582

Category: Medical

Page: 445

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Author: Roxy Peck,Tom Short

Publisher: Cengage Learning

ISBN: 1337558087

Category: Mathematics

Page: 729

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ISBN: 9781420011289

Category: Computers

Page: 417

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*Patterns for Learning from Data at Scale*

Author: Sandy Ryza,Uri Laserson,Sean Owen,Josh Wills

Publisher: "O'Reilly Media, Inc."

ISBN: 1491972904

Category: Computers

Page: 280

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*Processing Techniques in Sensor Networks*

Author: João Gama,Mohamed Medhat Gaber

Publisher: Springer Science & Business Media

ISBN: 3540736794

Category: Computers

Page: 244

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*Learning from Data*

Author: Simon Vaughan

Publisher: Cambridge University Press

ISBN: 1107434211

Category: Science

Page: 312

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*The Art and Science of Learning from Data*

Author: Maria Ripol,Megan Mocko,Alan Agresti,Christine Franklin

Publisher: Prentice Hall

ISBN: 9780136037354

Category: Mathematics

Page: 176

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*An Introduction To Statistical Reasoning*

Author: Arthur Glenberg,Matthew Andrzejewski

Publisher: Routledge

ISBN: 1136676627

Category: Education

Page: 580

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Author: Moamar Sayed-Mouchaweh

Publisher: Springer

ISBN: 331925667X

Category: Computers

Page: 75

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*an introduction to statistical reasoning*

Author: Beth Ann Haines,Arthur M. Glenberg

Publisher: Psychology Press

ISBN: 9780805817850

Category: Computers

Page: 552

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Author: CTI Reviews

Publisher: Cram101 Textbook Reviews

ISBN: 1467278580

Category: Education

Page: 32

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Publisher: Information Gatekeepers Inc

ISBN: N.A

Category:

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ISBN: 1467254088

Category: Education

Page: 32

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*Learning from Data*

Author: Olsen Peck,Roxy Peck

Publisher: N.A

ISBN: 9781285085241

Category: Mathematical statistics

Page: 848

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Publisher: Cram101 Textbook Reviews

ISBN: 1497091950

Category: Education

Page: 86

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*Concepts, Tools, and Techniques to Build Intelligent Systems*

Author: Aurélien Géron

Publisher: "O'Reilly Media, Inc."

ISBN: 1491962267

Category: Computers

Page: 572

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*The Art and Science of Learning from Data*

Author: Maureen Petkewich,Peter Flanagan-Hyde,Jack Morse,Jennifer Lewis Priestley,Michael Kowalski,Debra Hydorn

Publisher: Prentice Hall

ISBN: 9780136036159

Category: Mathematics

Page: 1080

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