An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and theories, including Boosting, Bagging, Random Forest, averaging and voting schemes, the Stacking method, mixture of experts, and diversity measures. It also discusses multiclass extension, noise tolerance, error-ambiguity and bias-variance decompositions, and recent progress in information theoretic diversity. Moving on to more advanced topics, the author explains how to achieve better performance through ensemble pruning and how to generate better clustering results by combining multiple clusterings. In addition, he describes developments of ensemble methods in semi-supervised learning, active learning, cost-sensitive learning, class-imbalance learning, and comprehensibility enhancement.

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
Clarifies both traditional methods and current, popular algorithms (e.g. neural nets, random forests)
Written by two world-leading researchers
Addressed to all fields that work with data

Joel, Apress, Blogs, and Blooks ...I was learning the hard way about how to be a publisher and probably spending way too much time looking at web sites and programming than I should have in response to that. Anyway, one day I came across this web site called , which was run by a guy with strong opinions and an unusual, clever writing style, along with a willingness to take on the conventional wisdom. In particular, he was writing this ongoing series about how bad most user interfaces were--mostly because programmers by and large knew, as Joel and I would say, using the same Yiddish--derived NYC vernacular that we both share, "bupkis" about what users really want. And I, like many, was hooked both by the series and the occasional random essay that Joel wrote. And then I had this epiphany: I'm a publisher, I like reading his stuff, why not turn it into a book?...Read the complete Foreword -- Gary Cornell, Cofounder, Apress Since the release of the bestselling title Joel on Software in 2004, requests for a sequel have been relentless. So, we went back to the famed JoelonSoftware.com archives and pulled out a new batch of favorites, many of which have been downloaded over one million times. With Joel's newest book, More Joel on Software, you'll get an even better (not to mention updated) feast of Joel's opinions and impressions on software development, software design, running a software business, and so much more. This is a new selection of essays from the author's web site, http://www.joelonsoftware.com. Joel Spolsky started his weblog in March 2000 in order to offer his insights, based on years of experience, on how to improve the world of programming. This weblog has become infamous among the programming world, and is linked to more than 600 other web sites and translated into 30+ languages! Spolsky's extraordinary writing skills, technical knowledge, and caustic wit have made him a programming guru. With the success of Joel on Software, there has been a strong demand for additional gems and advice, and this book is the answer to those requests. Containing a collection of all--new articles from the original, More Joel on Software has even more of an edge than the original, and the tips for running a business or managing people have far broader application than the software industry. We feel it is safe to say that this is the most useful book you will buy this year. What you'll learn * The best approaches to managing and hiring extraordinary people * Advice for those interested in the software industry as a career and for managers who want to get them * Joel's unique impressions of how to create products and design--good and bad * An "in the trenches" look at how to start and run an effective software business (or any business for that matter) * A true sense of what it takes to create a differentiated, unique, motivated organization Who this book is for Anyone interested in the software business will truly enjoy this book, but in particular this should be required reading for managers of technical businesses. Table of Contents * My First BillG Review * Finding Great Developers * A Field Guide to Developers * Three Management Methods (Introduction) * The Command and Control Management Method * The Econ 101 Management Method * The Identity Management Method * The Perils of JavaSchools * Talk at Yale * Advice for Computer Science College Students * Font Smoothing, Anti-Aliasing, and Subpixel Rendering * A Game of Inches * The Big Picture * Choices = Headaches * It's Not Just Usability * Building Communities with Software * Martian Headsets * Why Are the Microsoft Office File Formats So Complicated? * Where There's Muck, There's Brass * Evidence-Based Scheduling * Strategy Letter VI * Can Your Programming Language Do This? * Making Wrong Code Look Wrong * Foreword to Eric Sink on the Business of Software * Foreword to Micro-ISV: From Vision to Reality * Hitting the High Notes * Bionic Office * Up the Tata Without a Tutu * Simplicity * Rub a Dub Dub * Top Twelve Tips for Running a Beta Test * Seven Steps to Remarkable Customer Service * Picking a Ship Date * Camels and Rubber Duckies * Five Whys * Set Your Priorities

This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields. Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required. 0201157675B07092001

This book reflects decades of important research on the mathematical foundations of speech recognition. It focuses on underlying statistical techniques such as hidden Markov models, decision trees, the expectation-maximization algorithm, information theoretic goodness criteria, maximum entropy probability estimation, parameter and data clustering, and smoothing of probability distributions. The author's goal is to present these principles clearly in the simplest setting, to show the advantages of self-organization from real data, and to enable the reader to apply the techniques.

Analysis of Ordinal Categorical Data Alan Agresti Statistical Science Now has its first coordinated manual of methods for analyzing ordered categorical data. This book discusses specialized models that, unlike standard methods underlying nominal categorical data, efficiently use the information on ordering. It begins with an introduction to basic descriptive and inferential methods for categorical data, and then gives thorough coverage of the most current developments, such as loglinear and logit models for ordinal data. Special emphasis is placed on interpretation and application of methods and contains an integrated comparison of the available strategies for analyzing ordinal data. This is a case study work with illuminating examples taken from across the wide spectrum of ordinal categorical applications. 1984 (0 471-89055-3) 287 pp. Regression Diagnostics Identifying Influential Data and Sources of Collinearity David A. Belsley, Edwin Kuh and Roy E. Welsch This book provides the practicing statistician and econometrician with new tools for assessing the quality and reliability of regression estimates. Diagnostic techniques are developed that aid in the systematic location of data points that are either unusual or inordinately influential; measure the presence and intensity of collinear relations among the regression data and help to identify the variables involved in each; and pinpoint the estimated coefficients that are potentially most adversely affected. The primary emphasis of these contributions is on diagnostics, but suggestions for remedial action are given and illustrated. 1980 (0 471-05856-4) 292 pp. Applied Regression Analysis Second Edition Norman Draper and Harry Smith Featuring a significant expansion of material reflecting recent advances, here is a complete and up-to-date introduction to the fundamentals of regression analysis, focusing on understanding the latest concepts and applications of these methods. The authors thoroughly explore the fitting and checking of both linear and nonlinear regression models, using small or large data sets and pocket or high-speed computing equipment. Features added to this Second Edition include the practical implications of linear regression; the Durbin-Watson test for serial correlation; families of transformations; inverse, ridge, latent root and robust regression; and nonlinear growth models. Includes many new exercises and worked examples. 1981 (0 471-02995-5) 709 pp.

A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field.

A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.

This book uses the methodology of artificial intelligence to investigate the phenomena of visual motion perception: how the visual system constructs descriptions of the environment in terms of objects, their three-dimensional shape, and their motion through space, on the basis of the changing image that reaches the eye. The author has analyzed the computations performed in the course of visual motion analysis. Workable schemes able to perform certain tasks performed by the visual system have been constructed and used as vehicles for investigating the problems faced by the visual system and its methods for solving them.Two major problems are treated: first, the correspondence problem, which concerns the identification of image elements that represent the same object at different times, thereby maintaining the perceptual identity of the object in motion or in change. The second problem is the three-dimensional interpretation of the changing image once a correspondence has been established.The author's computational approach to visual theory makes the work unique, and it should be of interest to psychologists working in visual perception and readers interested in cognitive studies in general, as well as computer scientists interested in machine vision, theoretical neurophysiologists, and philosophers of science.

Generative Programming (GP) offers the promise of moving from "one-of-a-kind" software systems to the semi-automated manufacture of wide varieties of software -- essentially, an assembly line for software systems. GP's goal is to model software system families and build software modules such that, given particular requirements specs, highly customized and optimized intermediate or end products can be constructed on demand. This is the first book to cover Generative Programming in depth. The authors, leaders in their field, introduce the two-stage GP development cycle: one stage for designing and implementing a generative domain model, and another for using the model to build concrete systems. They review key differences between generative modeling and processes used for "one-of-a-kind" systems. Next, they introduce key GP concepts such as feature models, and demonstrate "generic programming" techniques for creating components which lend themselves to easy combination and reuse. The book also introduces Aspect Oriented Programming, which allows developers to solve key recurring problems in traditional O-O development; and presents metaprogramming techniques for building powerful program generators. Three detailed case studies demonstrate the entire generative development cycle, from analysis to implementation.

In the first edition of Thinking in C++, Bruce Eckel synthesized years of C++ teaching and programming experience into a beautifully structured course in making the most of the language. It became an instant classic, winning the 1995 Software Development Jolt Cola Award for best book of the year. Now, Eckel has thoroughly rewritten Thinking in C++ to reflect the final ANSI/ISO C++ standard. Every page has been revisited and rethought, with many new examples and exercises -- all designed to help you understand C++ "down to the bare metal," so you can solve virtually any problem. Eckel starts with a detailed look at objects, showing how C++ programs can be constructed from off-the-shelf object libraries. This edition includes a new, chapter-length overview of the C features that are used in C++ -- plus a new CD-ROM containing an outstanding C seminar that covers all the foundations developers need before they can truly take advantage of C++. Eckel then walks through initialization and cleanup; function overloading and default arguments; constants; inline functions; name control; references and the copy constructor; operator overloading; and more. There are chapters on dynamic object creation; inheritance and composition; polymorphism and virtual functions, and templates. (Bonus coverage of string, templates, and the Standard Template Library, can be found at Eckel's web site.) Every chapter contains many modular, to-the-point examples, plus exercises based on Eckel's extensive experience teaching C++ seminars. Put simply, Eckel has made an outstanding book on C++ even better.

A Nobel Prize-winning scientist and a leading brain researcher show how the brain creates conscious experience In A Universe of Consciousness, Gerald Edelman builds on the radical ideas he introduced in his monumental trilogy-Neural Darwinism, Topobiology, and The Remembered Present-to present for the first time an empirically supported full-scale theory of consciousness. He and the neurobiolgist Giulio Tononi show how they use ingenious technology to detect the most minute brain currents and to identify the specific brain waves that correlate with particular conscious experiences. The results of this pioneering work challenge the conventional wisdom about consciousness.

Mark Pilgrim's Dive Into Python 3 is a hands-on guide to Python 3 (the latest version of the Python language) and its differences from Python 2. As in the original book, Dive Into Python, each chapter starts with a real, complete code sample, proceeds to pick it apart and explain the pieces, and then puts it all back together in a summary at the end.
This book includes:
* Example programs completely rewritten to illustrate powerful new concepts now available in Python 3: sets, iterators, generators, closures, comprehensions, and much more
* A detailed case study of porting a major library from Python 2 to Python 3
* A comprehensive appendix of all the syntactic and semantic changes in Python 3
This is the perfect resource for you if you need to port applications to Python 3, or if you like to jump into languages fast and get going right away.
What you'll learn
* Understand Python 3 code by seeing it broken down and explained
* Make full use of the latest Python features such as iterators, generators, closures, classes and comprehensions
* Refactor existing code to improve maintainability
* Learn how to serialize Python objects with the pickle protocol and JSON format
* Learn how to package your own Python libraries and upload them to the Python Package Index to share your projects with Python developers worldwide
* Use Python 3 to consume HTTP web services
* Port existing Python applications to Python 3 by following a case study for a major library
Who is this book for?
* Anyone who wants to learn the latest version of Python in a fast, hands-on fashion
* Existing Python programmers who want to learn quickly how to make the most of the features of the latest version of Python and port their code to it
* Programmers coming from other languages wanting a fast introduction to Python that gets them thinking about advanced concepts quickly