For many applications, a randomized algorithm is either the simplest or the fastest algorithm available, and sometimes both. This book introduces the basic concepts in the design and analysis of randomized algorithms. The first part of the text presents basic tools such as probability theory and probabilistic analysis that are frequently used in algorithmic applications. Algorithmic examples are also given to illustrate the use of each tool in a concrete setting. In the second part of the book, each chapter focuses on an important area to which randomized algorithms can be applied, providing a comprehensive and representative selection of the algorithms that might be used in each of these areas. Although written primarily as a text for advanced undergraduates and graduate students, this book should also prove invaluable as a reference for professionals and researchers.
Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.
This volume contains the proceedings of the Workshop on On-line Algorithms held at the DIMACS Center at Rutgers University in February 1991. Presenting new results in the theory of on-line algorithms, the articles discuss a broad range of problems. Most of the papers are based on competitive (worst-case) analysis of on-line algorithms, but some consider alternative approaches. This book is aimed primarily at specialists in algorithm analysis, but most of the articles present clear expositions of previous work.
This book constitutes the thoroughly refereed post-proceedings of the Second Workshop on Intelligent Techniques in Web Personalization, ITWP 2003, held in Acapulco, Mexico in August 2003 as part of IJCAI 2003, the 18th International Joint Conference on Artificial Intelligence. The 17 revised full papers presented were carefully selected and include extended versions of some of the papers presented at the ITWP 2003 workshop as well as a number of invited chapters by leading researchers in the field of Intelligent Techniques for Web Personalization. The papers are organized in topical sections on user modelling, recommender systems, enabling technologies, personalized information access, and systems and applications.
This book constitutes the refereed proceedings of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2006, held in Singapore in April 2006. The 67 revised full papers and 33 revised short papers presented together with 3 invited talks were carefully reviewed and selected from 501 submissions. The papers are organized in topical sections on Classification, Ensemble Learning, Clustering, Support Vector Machines, Text and Document Mining, Web Mining, Bio-Data Mining, and more.
Abstract: "Speculative execution of code is becoming a key technique for enhancing the performance of pipeline processors. In this work we study schemes that predict the execution path of a program based on the history of branch executions. Building on previous work, we present a model for analyzing the effective speedup from pipelining using various schemes for speculative execution. We follow this with stochastic analyses of various speculative execution schemes. Finally, we conclude with simulations covering several of the settings we study."
Learn how the crisis over digital privacy and manipulation evolved in this “utterly fascinating” look at the growth of data mining and analysis (Seattle Post-Intelligencer). Award-winning journalist Stephen Baker traces the rise of the “global math elite”: computer scientists who invent ways to not only record our behavior, but also to predict and alter it. Nowadays, we don’t need to be online to create a digital trail; we do it simply by driving through an automated tollbooth or shopping with a credit card. As massive amounts of information are collected, sifted, and analyzed, we all become targets of those who want to influence everything from what we buy to how we vote. Clear and “highly readable,” The Numerati is a look at the origins of our present-day world, the possibilities of the future, and those who—whether with good or bad intentions—profile us as workers, consumers, citizens, or potential terrorists (The Wall Street Journal).
Clustering is an important technique for discovering relatively dense sub-regions or sub-spaces of a multi-dimension data distribution. Clus tering has been used in information retrieval for many different purposes, such as query expansion, document grouping, document indexing, and visualization of search results. In this book, we address issues of cluster ing algorithms, evaluation methodologies, applications, and architectures for information retrieval. The first two chapters discuss clustering algorithms. The chapter from Baeza-Yates et al. describes a clustering method for a general metric space which is a common model of data relevant to information retrieval. The chapter by Guha, Rasto...
When you think about how far and fast computer science has progressed in recent years, it's not hard to conclude that a seven-year old handbook may fall a little short of the kind of reference today's computer scientists, software engineers, and IT professionals need. With a broadened scope, more emphasis on applied computing, and more than 70 chapters either new or significantly revised, the Computer Science Handbook, Second Edition is exactly the kind of reference you need. This rich collection of theory and practice fully characterizes the current state of the field and conveys the modern spirit, accomplishments, and direction of computer science. Highlights of the Second Edition: Coverag...