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Computer Science 2006 - Learning and Information Retrieval
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Ronen Feldman, James Sanger
Text mining tries to solve the crisis of information overload by combining techniques from data mining, machine learning, natural language processing, information retrieval, and knowledge management. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, this book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches. Finally, it explores current real-world, mission-critical applications of text mining and link detection in such varied fields as M&A business intelligence, genomics research and counter-terrorism activities.
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Nicolo Cesa-Bianchi, Gabor Lugosi
This important new text and reference for researchers and students in machine learning, game theory, statistics and information theory offers the first comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that often reveals new and intriguing connections. Old and new forecasting methods are described in a mathematically precise way in order to characterize their theoretical limitations and possibilities.
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Jon Doyle
This book deploys the mathematical axioms of modern rational mechanics to understand minds as mechanical systems that exhibit actual, not metaphorical, forces, inertia, and motion. Using precise mental models developed in artificial intelligence the author analyzes motivation, attention, reasoning, learning, and communication in mechanical terms. These analyses provide psychology and economics with new characterizations of bounded rationality; provide mechanics with new types of materials exhibiting the constitutive kinematic and dynamic properties characteristic of different kinds of minds; and provide philosophy with a rigorous theory of hybrid systems combining discrete and continuous mechanical quantities. The resulting mechanical reintegration of the physical sciences that characterize human bodies and the mental sciences that characterize human minds opens traditional philosophical and modern computational questions to new paths of technical analysis.
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Thomas Dean
Thomas Dean explores a wide range of fundamental topics in computer science, from digital logic and machine language to artificial intelligence and the World Wide Web, explaining how computers and computer programs work and how the various subfields of computer science are interconnected. Dean touches on a number of questions including: How can a computer learn to recognize junk email? What happens when you click on a link in a browser? How can you program a robot to do two things at once? Are there limits to what computers can do?
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Richard K. Belew
The World Wide Web is rapidly filling with more text than anyone could have imagined a short time ago. However, the task of determining which data is relevant has become appreciably harder. In this original new work Richard Belew brings a cognitive science perspective to the study of information as a computer science discipline. He introduces the idea of Finding Out About (FOA), the process of actively seeking out information relevant to a topic of interest. Belew describes all facets of FOA, ranging from creating a good characterization of what the user seeks to evaluating the successful performance of search engines. His volume clearly shows how to build many of the tools that are useful for searching collections of text and other media. While computer scientists make up the book's primary audience, Belew skillfully presents technical details in a manner that makes important themes accessible to readers more comfortable with words than equations. (A CD is included with the text.)
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David J. C. MacKay
Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography.
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Nello Cristianini, John Shawe-Taylor
This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software make it an ideal starting point for further study.
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Yang Xiang
Probalistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become an active field of research and practice in artifical intelligence, operations research and statistics in the last two decades. The success of this technique in modeling intelligent decision support systems under the centralized and single-agent paradim has been striking. In this book, the author extends graphical dependence models to the distributed and multi-agent paradigm. He identifies the major technical challenges involved in such an endeavor and presents the results gleaned from a decade's research.
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John Shawe-Taylor, Nello Cristianini
This book provides professionals with a large selection of algorithms, kernels and solutions ready for implementation and suitable for standard pattern discovery problems in fields such as bioinformatics, text analysis and image analysis. It also serves as an introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.
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Chitta Baral
Knowledge management and knowledge-based intelligence are areas of importance in today's economy and society, and their exploitation requires representation via the development of a declarative interface whose input language is based on logic. Chitta Baral demonstrates how to write programs that behave intelligently by giving them the ability to express knowledge and reason about it. He presents a language, AnsProlog*, for both knowledge representation and reasoning, and declarative problem solving. Many of the results have never appeared before in book form but are organized here for those wishing to learn more about the subject, either in courses or through self-study.
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C. J. van Rijsbergen
Keith Van Rijsbergen demonstrates how different models of information retrieval (IR) can be combined in the same framework used to formulate the general principles of quantum mechanics. All the standard results can be applied to address problems in IR, such as pseudo-relevance feedback, relevance feedback and ostensive retrieval. The relation with quantum computing is examined. Appendices with background material on physics and mathematics are also included.
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Patrick Doreian, Vladimir Batagelj, Anuska Ferligoj
After establishing its mathematical foundations, this integrated study of blockmodeling, the most frequently used technique in social network analysis, generalizes blockmodeling for the examination of many network structures. It also includes a broad introduction to cluster analysis. The authors propose direct optimizational approaches to blockmodeling which yield blockmodels that best fit the data, and create the potential for many generalizations and a deductive use of blockmodeling.
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Wouter de Nooy, Andrej Mrvar, Vladimir Batagelj
This is the first textbook on social network analysis integrating theory, applications, and professional software for performing network analysis (Pajek). Pajek software and datasets for all examples are freely available, so the reader can learn network analysis by doing it. In addition, each chapter offers case studies for practicing network analysis. The book will enable the reader to gain the knowledge, skills, and tools to apply social network analysis in all social sciences, ranging from anthropology and sociology to business administration and history.
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