The Handbook of Latent Semantic Analysis is the authoritative reference for the theory behind Latent Semantic Analysis (LSA), a burgeoning mathematical method used to analyze how words make meaning, with the desired outcome to program machines to understand human commands via natural language rather than strict programming protocols. The first book of its kind to deliver such a comprehensive analysis, this volume explores every area of the method and combines theoretical implications as well as practical matters of LSA. Readers are introduced to a powerful new way of understanding language phenomena, as well as innovative ways to perform tasks that depend on language or other complex systems. The Handbook clarifies misunderstandings and pre-formed objections to LSA, and provides examples of exciting new educational technologies made possible by LSA and similar techniques. It raises issues in philosophy, artificial intelligence, and linguistics, while describing how LSA has underwritten a range of educational technologies and information systems. Alternate approaches to language understanding are addressed and compared to LSA. This work is essential reading for anyone-newcomers to this area and experts alike-interested in how human language works or interested in computational analysis and uses of text. Educational technologists, cognitive scientists, philosophers, and information technologists in particular will consider this volume especially useful.

Geared towards both future and practising social scientists, this book takes an applied approach and offers readers empirical examples to illustrate key concepts. It includes: applied coverage of a topic that has traditionally been discussed from a theoretical standpoint; empirical examples to illustrate key concepts; a web appendix that provides readers with the data and the R-code for the examples used in the book.

A navy is a state's main instrument of maritime force. What it should do, what doctrine it holds, what ships it deploys, and how it fights are determined by practical political and military choices in relation to national needs. Choices are made according to the state's goals, perceived threat, maritime opportunity, technological capabilities, practical experience, and, not the least, the way the sea service defines itself and its way of war. This book is a history of the modern U.S. Navy. It explains how the Navy, in the century after 1890, was formed and reformed in the interaction of purpose, experience, and doctrine.

A hands on guide to web scraping and text mining for bothbeginners and experienced users of R
(1)Introduces fundamental concepts of the main architecture of theweb and databases and covers HTTP, HTML, XML, JSON, SQL.
(2)Provides basic techniques to query web documents and data sets(XPath and regular expressions).
(3)An extensive set of exercises are presented to guide thereader through each technique.
(4)Explores both supervised and unsupervised techniques as well asadvanced techniques such as data scraping and text management.
(5)Case studies are featured throughout along with examples foreach technique presented.
(6)R code and solutions to exercises featured in thebook are provided on a supporting website.

In her groundbreaking bestselling history of the class system in America, Nancy Isenberg upends histroy as we know it by taking on our comforting myths about equality and uncovering the crucial legacy of the ever-present, always embarrassing—if occasionally entertaining—poor white trash.
“When you turn an election into a three-ring circus, there’s always a chance that the dancing bear will win,” says Isenberg of the political climate surrounding Sarah Palin. And we recognize how right she is today. Yet the voters boosting Trump have been a permanent part of our American fabric, argues Isenberg.
The wretched and landless poor have existed from the time of the earliest British colonial settlement to today's hillbillies. They were alternately known as “waste people,” “offals,” “rubbish,” “lazy lubbers,” and “crackers.” By the 1850s, the downtrodden included so-called “clay eaters” and “sandhillers,” known for prematurely aged children distinguished by their yellowish skin, ragged clothing, and listless minds.
Surveying political rhetoric and policy, popular literature and scientific theories over four hundred years, Isenberg upends assumptions about America’s supposedly class-free society––where liberty and hard work were meant to ensure real social mobility. Poor whites were central to the rise of the Republican Party in the early nineteenth century, and the Civil War itself was fought over class issues nearly as much as it was fought over slavery. Reconstruction pitted poor white trash against newly freed slaves, which factored in the rise of eugenics–-a widely popular movement embraced by Theodore Roosevelt that targeted poor whites for sterilization. These poor were at the heart of New Deal reforms and LBJ’s Great Society; they haunt us in reality TV shows like Here Comes Honey Boo Boo and Duck Dynasty. Marginalized as a class, white trash have always been at or near the center of major political debates over the character of the American identity.
We acknowledge racial injustice as an ugly stain on our nation’s history. With Isenberg’s landmark book, we will have to face the truth about the enduring, malevolent nature of class as well.

A portrait of a great American dynasty and its legacy in business, technology, the arts, and philanthropy Meyer Guggenheim, a Swiss immigrant, founded a great American business dynasty. At their peak in the early twentieth century, the Guggenheims were reckoned among America's wealthiest, and the richest Jewish family in the world after the Rothschilds. They belonged to Our Crowd, that tight social circle of New York Jewish plutocrats, but unlike the others -- primarily merchants and financiers -- they made their money by extracting and refining copper, silver, lead, tin, and gold. The secret of their success, the patriarch believed, was their unity, and in the early years Meyer's seven sons, under the leadership of Daniel, worked as one to expand their growing mining and smelting empire. Family solidarity eventually decayed (along with their Jewish faith), but even more damaging was the paucity of male heirs as Meyer and the original set of brothers passed from the scene. In the third generation, Harry Guggenheim, Daniel's son, took over leadership and made the family a force in aviation, publishing, and horse-racing. He desperately sought a successor but tragically failed and was forced to watch as the great Guggenheim business enterprise crumbled. Meanwhile, "Guggenheim" came to mean art more than industry. In the mid-twentieth century, led by Meyer's son Solomon and Solomon's niece Peggy, the Guggenheims became the agents of modernism in the visual arts. Peggy, in America during the war years, midwifed the school of abstract expressionism, which brought art leadership to New York City. Solomon's museum has been innovative in spreading the riches of Western art around the world. After the generation of Harry and Peggy, the family has continued to produce many accomplished members, such as publisher Roger Straus II and archaeologist Iris Love. In The Guggenheims , through meticulous research and absorbing prose, Irwin Unger, the winner of a Pulitzer Prize in history, and his wife, Debi Unger, convey a unique and remarkable story -- epic in its scope -- of one family's amazing rise to prominence.

Many of the complex problems faced by decision makers involve multiple conflicting objectives. This book describes how a confused decision maker, who wishes to make a reasonable and responsible choice among alternatives, can systematically probe his true feelings in order to make those critically important, vexing trade-offs between incommensurable objectives. The theory is illustrated by many real concrete examples taken from a host of disciplinary settings. The standard approach in decision theory or decision analysis specifies a simplified single objective like monetary return to maximise. By generalising from the single objective case to the multiple objective case, this book considerably widens the range of applicability of decision analysis.

Two-sided matching provides a model of search processes such as those between firms and workers in labor markets or between buyers and sellers in auctions. This book gives a comprehensive account of recent results concerning the game-theoretic analysis of two-sided matching. The focus of the book is on the stability of outcomes, on the incentives that different rules of organization give to agents, and on the constraints that these incentives impose on the ways such markets can be organized. The results for this wide range of related models and matching situations help clarify which conclusions depend on particular modeling assumptions and market conditions, and which are robust over a wide range of conditions. 'This book chronicles one of the outstanding success stories of the theory of games, a story in which the authors have played a major role: the theory and practice of matching markets ...The authors are to be warmly congratulated for this fine piece of work, which is quite unique in the game-theoretic literature.' From the Foreword by Robert Aumann

Master Python and become a programmer-even if you never thought you could! This breakthrough book and CD can help practically anyone get started in programming. It's called "The Hard Way," but it's really quite simple. What's "hard" is this: it requires discipline, practice, and persistence. Zed A. Shaw teaches the Python programming language through a series of 52 brilliantly-crafted exercises-all formatted consistently, and most no longer than two pages (including "extra credit"). Just read each exercise, type in its sample code precisely (no copy-and-paste!), and make the programs run. As you read, type, fix your mistakes, and watch the results, you'll learn how software works, how programming works, what good programs look like, and how to read, write, and see code. You'll discover how to spot crucial differences that fundamentally affect program behavior, and you'll learn everything you need to know about Python logic, input/output, variables, and functions. Above all, you'll learn the attention to detail that is indispensable to successful programming (and so much else in life). At first, yes, it can be difficult. But it gets easier. And Shaw offers plenty of extra guidance and insight through 5+ full hours of teaching video on the accompanying CD. As Shaw's thousands of online readers and fans will attest, the moment will come when you just "get it"-and that moment feels great. Nothing important comes without discipline, practice, and persistence. But, with Learn Python the Hard Way, readers who bring those qualities to programming will master it-and they will reap the rewards, both personally and in their careers.

This book provides evidence-based answers to the key questions asked by marketers every day. Tackling issues such as how brands grow, how advertising really works, what price promotions really do and how loyalty programs really affect loyalty, How Brands Grow presents decades of research in a style that is written for marketing professionals to grow their brands. It is the first book to present these laws in context and to explore their meaning and application. The most distinctive element to this book is that the laws presented are tried and tested; they have been found to hold over varied conditions, time and countries. This is contra to most marketing texts and indeed, much information provides evidence that much modern marketing theory is far from soundly based.

The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

This book comprehensively discusses the neural network models from a statistical mechanics perspective. It starts with one of the most influential developments in the theory of neural networks: Hopfield's analysis of networks with symmetric connections using the spin system approach and using the notion of an energy function from physics. Introduction to the Theory of Neural Computation uses these powerful tools to analyze neural networks as associative memory stores and solvers of optimization problems. A detailed analysis of multi-layer networks and recurrent networks follow. The book ends with chapters on unsupervised learning and a formal treatment of the relationship between statistical mechanics and neural networks. Little information is provided about applications and implementations, and the treatment of the material reflects the background of the authors as physicists. However the book is essential for a solid understanding of the computational potential of neural networks. Introduction to the Theory of Neural Computation assumes that the reader is familiar with undergraduate level mathematics, but does not have any background in physics. All of the necessary tools are introduced in the book.

In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs -- -kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics.Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.