This happens automatically when following agood, simple set of guidelines.*They improve development speed, because the programmer doesn't need toalways make decisions starting from first principles.*They enhance teamwork by eliminating needless debates on inconsequentialissues and by making it easy for teammates to read and maintain each other'scode.The coding standards introduced by this book are a collection of guidelines forwriting high-quality C++ code.***They are the distilled conclusions of a rich collective experience of the C++community. Until now, this body of knowledge has been available only asfolklore or spread in bits and pieces throughout books.

    There are many sources too for basic information on the extensions to the WWW that permit content to be expressed in natural language yet used by software agents to easily find, share and integrate information. Until now individuals engaged in creating ontologies-- formal descriptions of the concepts, terms, and relationships within a given knowledge domain-- have had no sources beyond the technical standards documents.Semantic Web for the Working Ontologist transforms this information into the practical knowledge that programmers and subject domain experts need. Authors Allemang and Hendler begin with solutions to the basic problems, but don't stop there: they demonstrate how to develop your own solutions to problems of increasing complexity and ensure that your skills will keep pace with the continued evolution of the Semantic Web.

    In the beginning there was the Holy Bible2 Which was a very good book indeed, but so many things happened since the beginning,3 That Maybe it was time for another bible,4 So a punk from Philadelphia wrote a new one,5 And so it is called The Boomer Bible,6 So there.7 And Its Past Testament tells the history of the world, including the Book of Greeks, Book of Brits, Book of Yanks, Book of Russkies, and all the other self-proclaimed Chosen Nations,8 And people sticking each other with pointed sticks, and acting up, which is called civilization,9 And also about religion and art and movies and literature, and TV, and so forth, which is why there are also the Books of Pnowlege,10 Including Psongs, Psayings, and Psomethings,11 Written just like the other Bible but without any big unpronounceable words,12 So that you and I might truly understand it,13 For a change.14 And Its Present Testament tells about the coming of Harry, and The Way of Harry,15 Who may be the messiah everybody has been waiting for,16 Unless he really isn't,17 Which is hard to say,18 So there.19 And there is also The Book of Harrier Brayer together with the Harrier Hymnal,20 And another Testament too,21 And Concordance, and a lenticular hand on the cover.22 And It is not for the faint of heart,23 Or the easily offended,24 Or the priggish or the prudish,25 But who cares,26 Because neither was Candide, or Swift's A Modest Proposal, or Rabelais, or Lenny Bruce28 Or all the other satires and satirists who felt the need to warn us when we have gone astray,29 Which we have,29 Which you'll know all about,31 If you read your Boomer Bible,32 Or there.Over 87,000 copies in print.

    '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.

    Delivering a searing indictment of the political leaders who have led us astray, Scarborough inspires conservatives to reclaim their heritage by drawing upon the strength of the movement’s rich history.With independent thinking and straight talk, Scarborough explains:• How Washington and Wall Street conspired to create the housing bubble that caused America’s financial meltdown• How the “candidate of change” has not only maintained but accelerated the reckless spending policies that led us to this historic economic collapse• How Washington’s bailout culture will cripple America’s future if left unchecked• How Barack Obama’s stimulus plan devolved into a socialist spending spree that would make FDR and LBJ shudder• And how conservatives need to take a closer look at Ronald Reagan’s political career before claiming his great legacyA fearlessly argued conservative manifesto that brings American conservatism into the twenty-first century, The Last Best Hope is a must-read for all who care about the direction America is heading.

    Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you're ready to get started.Use R's default graphics for quick exploration of dataCreate a variety of bar graphs, line graphs, and scatter plotsSummarize data distributions with histograms, density curves, box plots, and other examplesProvide annotations to help viewers interpret dataControl the overall appearance of graphicsRender data groups alongside each other for easy comparisonUse colors in plotsCreate network graphs, heat maps, and 3D scatter plotsStructure data for graphing

    Widely praised and much-discussed, this classic book explores how cooperation can emerge in a world of self-seeking egoists—whether superpowers, businesses, or individuals—when there is no central authority to police their actions. The problem of cooperation is central to many different fields. Robert Axelrod recounts the famous computer tournaments in which the “cooperative” program Tit for Tat recorded its stunning victories, explains its application to a broad spectrum of subjects, and suggests how readers can both apply cooperative principles to their own lives and teach cooperative principles to others.

    Algorithms have made our lives more efficient, more entertaining, and, sometimes, better informed. At the same time, complex algorithms are increasingly violating the basic rights of individual citizens. Allegedly anonymized datasets routinely leak our most sensitive personal information; statistical models for everything from mortgages to college admissions reflect racial and gender bias. Meanwhile, users manipulate algorithms to "game" search engines, spam filters, online reviewing services, and navigation apps.Understanding and improving the science behind the algorithms that run our lives is rapidly becoming one of the most pressing issues of this century. Traditional fixes, such as laws, regulations and watchdog groups, have proven woefully inadequate. Reporting from the cutting edge of scientific research, The Ethical Algorithm offers a new approach: a set of principled solutions based on the emerging and exciting science of socially aware algorithm design. Michael Kearns and Aaron Roth explain how we can better embed human principles into machine code - without halting the advance of data-driven scientific exploration. Weaving together innovative research with stories of citizens, scientists, and activists on the front lines, The Ethical Algorithm offers a compelling vision for a future, one in which we can better protect humans from the unintended impacts of algorithms while continuing to inspire wondrous advances in technology.

    That book, which is in its 10th printing and has sold more than 80,000 copies, was such a resounding success that Hochswender has written an insightful new work -- at once a follow-up to the previous volume and a freestanding work of its own. A new breath of inspiration, "The Buddha in Your Rearview Mirror" speaks to the spiritual yearnings so many of us have amid the hustle and flux of contemporary life. The book is a sophisticated but accessible introduction to Buddhism as well as an in-depth study of Buddhism in the Samurai period. Hochswender again focuses on the philosophy of Nichiren and applies its principles to everyday issues ranging from health to careers to family problems. "The Buddha in Your Rearview Mirror" is both cogent and compelling -- informative history and inspiring self-help. Ideal for the novice or veteran Buddhist, the book will resonate with anyone interested in concrete methods for tapping into their own highest potential or enlightened self.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree- based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

    Hardcover published by Quadragle/The New York Times Book Co., third printing, August 1977, copyright 1975.

    It describes how to index your data, including types you definitely need to know such as MS Word, PDF, HTML, and XML. It introduces you to searching, sorting, filtering, and highlighting search results.Lucene powers search in surprising placesWhat's Inside- How to integrate Lucene into your applications- Ready-to-use framework for rich document handling- Case studies including Nutch, TheServerSide, jGuru, etc.- Lucene ports to Perl, Python, C#/.Net, and C++- Sorting, filtering, term vectors, multiple, and remote index searching- The new SpanQuery family, extending query parser, hit collecting- Performance testing and tuning- Lucene add-ons (hit highlighting, synonym lookup, and others)

    Distinguishing agents (e.g., molecules, cells, animals, and species) from their interactions (e.g., chemical reactions, immune system responses, sexual reproduction, and evolution), Flake argues that it is the computational properties of interactions that account for much of what we think of as beautiful and interesting. From this basic thesis, Flake explores what he considers to be today's four most interesting computational topics: fractals, chaos, complex systems, and adaptation.Each of the book's parts can be read independently, enabling even the casual reader to understand and work with the basic equations and programs. Yet the parts are bound together by the theme of the computer as a laboratory and a metaphor for understanding the universe. The inspired reader will experiment further with the ideas presented to create fractal landscapes, chaotic systems, artificial life forms, genetic algorithms, and artificial neural networks.

    Written by two of the best-known names in categorical logic, Conceptual Mathematics is the first book to apply categories to the most elementary mathematics. It thus serves two purposes: first, to provide a key to mathematics for the general reader or beginning student; and second, to furnish an easy introduction to categories for computer scientists, logicians, physicists, and linguists who want to gain some familiarity with the categorical method without initially committing themselves to extended study.