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Introduction to Information Retrieval
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.
Randomized Algorithms
For many applications a randomized algorithm is either the simplest algorithm available, or the fastest, or both. This tutorial presents the basic concepts in the design and analysis of randomized algorithms. The first part of the book presents tools from probability theory and probabilistic analysis that are recurrent in algorithmic applications. Algorithmic examples are given to illustrate the use of each tool in a concrete setting. In the second part of the book, each of the seven chapters focuses on one important area of application of randomized algorithms: data structures; geometric algorithms; graph algorithms; number theory; enumeration; parallel algorithms; and on-line algorithms. A comprehensive and representative selection of the algorithms in these areas is also given. This book should prove invaluable as a reference for researchers and professional programmers, as well as for students.
The Almanac of American Employers 2008
Includes information, such as benefit plans, stock plans, salaries, hiring and recruiting plans, training and corporate culture, growth, facilities, research and development, fax numbers, toll-free numbers and Internet addresses of companies that hire in America. This almanac provides a job market trends analysis.
Randomized Algorithms
This book presents basic tools from probability theory used in algorithmic applications, with concrete examples.
On-line Algorithms
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.
Intelligent Techniques for Web Personalization
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.
Introduction to Information Retrieval
Class-tested and up-to-date textbook for introductory courses on information retrieval.
Managing and Mining Graph Data
Managing and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety of important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by well known researchers in the field, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing. Managing and Mining Graph Data is designed for a varied audience composed of professors, researchers and practitioners in industry. This volume is also suitable as a reference book for advanced-level database students in computer science and engineering.
Theory of Computing and Systems
ISTCS '92, the Israel Symposium on the Theory of Computing and Systems, cameabout spontaneously as a result of informal interaction between a group of people who viewed the conference as an appropriate expression of Israeli strength in theoretical aspects of computing and systems. The enthusiasm that the symposium created resulted in the submission of a large number of extremely high quality papers, which led in turn to strict acceptance criteria. This volume contains nineteen selected papers representing the cream of Israeli talent in the field, on a variety of active and interesting topics in the theory of computing and systems.
Advances in Knowledge Discovery and Data Mining
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.
