Note about this ebook: This ebook exploits many advanced capabilities with images, hypertext, and interactivity and is optimized for EPUB3-compliant book readers, especially Apple's iBooks and browser plugins. These features may not work on all ebook readers. We organize things. We organize information, information about things, and information about information. Organizing is a fundamental issue in many professional fields, but these fields have only limited agreement in how they approach problems of organizing and in what they seek as their solutions. The Discipline of Organizing synthesizes insights from library science, information science, computer science, cognitive science, systems analysis, business, and other disciplines to create an Organizing System for understanding organizing. This framework is robust and forward-looking, enabling effective sharing of insights and design patterns between disciplines that weren’t possible before. The Professional Edition includes new and revised content about the active resources of the "Internet of Things," and how the field of Information Architecture can be viewed as a subset of the discipline of organizing. You’ll find: 600 tagged endnotes that connect to one or more of the contributing disciplines Nearly 60 new pictures and illustrations Links to cross-references and external citations Interactive study guides to test on key points The Professional Edition is ideal for practitioners and as a primary or supplemental text for graduate courses on information organization, content and knowledge management, and digital collections. FOR INSTRUCTORS: Supplemental materials (lecture notes, assignments, exams, etc.) are available at http://disciplineoforganizing.org. FOR STUDENTS: Make sure this is the edition you want to buy. There's a newer one and maybe your instructor has adopted that one instead.

A Practical Guide to Library of Congress Classification is a hands-on introduction to LC Classification. The book examines each part of the LCC call number and how it is assembled and guides the reader through each step of finding and constructing LCC class numbers in Classification Web (the primary resource used to access LCC). Chapter coverage is complete: 1. Introduction 2. Library of Congress Classification in a Nutshell 3. Breaking Down the Library of Congress Call Number 4. Dates 5. Cutters 6. LCC in Classification Web 7. Basic LCC Call Number Building 8. Advanced Call Number Building 9. Classifying Fiction in LCC 10. Finding and using LCC Resources Exercises at the end of most chapters give readers immediate practice with what they just learned. Answers to the exercises are provided at the end of the book. By the end of the book readers will be able to build an LCC call number on their own.

The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.

Support vector machines (SVMs), were originally formulated for two-class classification problems, and have been accepted as a powerful tool for developing pattern classification and function approximations systems. This book provides a unique perspective of the state of the art in SVMs by taking the only approach that focuses on classification rather than covering the theoretical aspects. The book clarifies the characteristics of two-class SVMs through their extensive analysis, presents various useful architectures for multiclass classification and function approximation problems, and discusses kernel methods for improving generalization ability of conventional neural networks and fuzzy systems. Ample illustrations, examples and computer experiments are included to help readers understand the new ideas and their usefulness. This book supplies a comprehensive resource for the use of SVMs in pattern classification and will be invaluable reading for researchers, developers & students in academia and industry.

Alfred C. Kinsey's revolutionary studies of human sexual behavior are world-renowned. His meticulous methods of data collection, from comprehensive entomological assemblies to personal sex history interviews, raised the bar for empirical evidence to an entirely new level. In The Classification of Sex, Donna J. Drucker presents an original analysis of Kinsey's scientific career in order to uncover the roots of his research methods. She describes how his enduring interest as an entomologist and biologist in the compilation and organization of mass data sets structured each of his classification projects. As Drucker shows, Kinsey's lifelong mission was to find scientific truth in numbers and through observation—and to record without prejudice in the spirit of a true taxonomist. Kinsey's doctoral work included extensive research of the gall wasp, where he gathered and recorded variations in over six million specimens. His classification and reclassification of Cynips led to the speciation of the genus that remains today. During his graduate training, Kinsey developed a strong interest in evolution and the links between entomological and human behavior studies. In 1920, he joined Indiana University as a professor in zoology, and soon published an introductory text on biology, followed by a coauthored field guide to edible wild plants. In 1938, Kinsey began teaching a noncredit course on marriage, where he openly discussed sexual behavior and espoused equal opportunity for orgasmic satisfaction in marital relationships. Soon after, he began gathering case histories of sexual behavior. As a pioneer in the nascent field of sexology, Kinsey saw that the key to its cogency was grounded in observation combined with the collection and classification of mass data. To support the institutionalization of his work, he cofounded the Institute for Sex Research at Indiana University in 1947. He and his staff eventually conducted over eighteen thousand personal interviews about sexual behavior, and in 1948 he published Sexual Behavior in the Human Male, to be followed in 1953 by Sexual Behavior in the Human Female. As Drucker's study shows, Kinsey's scientific rigor and his early use of data recording methods and observational studies were unparalleled in his field. Those practices shaped his entire career and produced a wellspring of new information, whether he was studying gall wasp wings, writing biology textbooks, tracing patterns of evolution, or developing a universal theory of human sexuality.

This book provides a unified approach for developing a fuzzy classifier and explains the advantages and disadvantages of different classifiers through extensive performance evaluation of real data sets. It thus offers new learning paradigms for analyzing neural networks and fuzzy systems, while training fuzzy classifiers. Function approximation is also treated and function approximators are compared.

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. 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, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

This book constitutes the refereed proceedings of the International Workshop on Multimedia Content Representation, Classification and Security, MRCS 2006. The book presents 100 revised papers together with 4 invited lectures. Coverage includes biometric recognition, multimedia content security, steganography, watermarking, authentication, classification for biometric recognition, digital watermarking, content analysis and representation, 3D object retrieval and classification, representation, analysis and retrieval in cultural heritage, content representation, indexing and retrieval, and more.