Learning From Data

Author: Yaser S. Abu-Mostafa
Editor:
ISBN: 9781600490064
Size: 16,69 MB
Format: PDF, ePub, Mobi
Read: 143
Download


Learning From Data

Author: Vladimir Cherkassky
Editor: John Wiley & Sons
ISBN: 9780470140512
Size: 14,52 MB
Format: PDF, Kindle
Read: 624
Download

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

Author: Doug Fisher
Editor: Springer Science & Business Media
ISBN: 1461224047
Size: 15,84 MB
Format: PDF, ePub, Docs
Read: 768
Download

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.

Learning From Data Streams

Author: João Gama
Editor: Springer Science & Business Media
ISBN: 3540736794
Size: 12,59 MB
Format: PDF
Read: 876
Download

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.

The Health Care Data Guide

Author: Lloyd P. Provost
Editor: John Wiley & Sons
ISBN: 1118085884
Size: 14,75 MB
Format: PDF, ePub, Docs
Read: 189
Download

The Health Care Data Guide is designed to help students andprofessionals build a skill set specific to using data forimprovement of health care processes and systems. Even experienceddata users will find valuable resources among the tools and casesthat enrich The Health Care Data Guide. Practical and step-by-step,this book spotlights statistical process control (SPC) and developsa philosophy, a strategy, and a set of methods for ongoingimprovement to yield better outcomes. Provost and Murray reveal how to put SPC into practice for awide range of applications including evaluating current processperformance, searching for ideas for and determining evidence ofimprovement, and tracking and documenting sustainability ofimprovement. A comprehensive overview of graphical methods in SPCincludes Shewhart charts, run charts, frequency plots, Paretoanalysis, and scatter diagrams. Other topics include stratificationand rational sub-grouping of data and methods to help predictperformance of processes. Illustrative examples and case studies encourage users toevaluate their knowledge and skills interactively and provideopportunity to develop additional skills and confidence indisplaying and interpreting data. Companion Web site: www.josseybass.com/go/provost

Statistics Learning From Data

Author: Roxy Peck
Editor: Cengage Learning
ISBN: 1337558087
Size: 20,76 MB
Format: PDF, ePub, Docs
Read: 426
Download

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.

Advanced Analytics With Spark

Author: Sandy Ryza
Editor: "O'Reilly Media, Inc."
ISBN: 1491972920
Size: 11,75 MB
Format: PDF
Read: 686
Download

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

Utility Based Learning From Data

Author: Craig Friedman
Editor: CRC Press
ISBN: 9781420011289
Size: 12,65 MB
Format: PDF, ePub, Mobi
Read: 459
Download

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.

Learning From Data

Author: Arthur Glenberg
Editor: Routledge
ISBN: 1136676627
Size: 10,79 MB
Format: PDF, Kindle
Read: 239
Download

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.

Statistics

Author: Alan Agresti
Editor: Prentice Hall
ISBN: 9780131357464
Size: 10,79 MB
Format: PDF, ePub, Docs
Read: 658
Download

CD-ROM contains: 19 applets including, sample from a population, sampling distributions, random numbers, long run probability demonstrations, mean versus median applet, standard deviation applet, hypothesis tests for a proportion and for a mean.