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Recent Advances in Applied Probability
Applied probability is a broad research area that is of interest to scientists in diverse disciplines in science and technology, including: anthropology, biology, communication theory, economics, epidemiology, finance, geography, linguistics, medicine, meteorology, operations research, psychology, quality control, sociology, and statistics. Recent Advances in Applied Probability is a collection of survey articles that bring together the work of leading researchers in applied probability to present current research advances in this important area. This volume will be of interest to graduate students and researchers whose research is closely connected to probability modelling and their applications. It is suitable for one semester graduate level research seminar in applied probability.
Algorithms and Complexity
This book constitutes the refereed proceedings of the 7th International Conference on Algorithms and Computation, CIAC 2010, held in Rome, Italy, in May 2010. The 30 revised full papers presented together with 3 invited papers were carefully reviewed and selected from 114 submissions. Among the topics addressed are graph algorithms I, computational complexity, graph coloring, tree algorithms and tree decompositions, computational geometry, game theory, graph algorithms II, and string algorithms.
Experimental Algorithms
This book constitutes the refereed proceedings of the 5th International Workshop on Experimental and Efficient Algorithms, WEA 2006, held in Menorca, Spain, May 2006. The book presents 26 revised full papers together with 3 invited talks. The application areas addressed include most fields applying advanced algorithmic techniques, such as combinatorial optimization, approximation, graph theory, discrete mathematics, scheduling, searching, sorting, string matching, coding, networking, and more.
Information Storage and Retrieval Systems
Chapter 1 places into perspective a total Information Storage and Retrieval System. This perspective introduces new challenges to the problems that need to be theoretically addressed and commercially implemented. Ten years ago commercial implementation of the algorithms being developed was not realistic, allowing theoreticians to limit their focus to very specific areas. Bounding a problem is still essential in deriving theoretical results. But the commercialization and insertion of this technology into systems like the Internet that are widely being used changes the way problems are bounded. From a theoretical perspective, efficient scalability of algorithms to systems with gigabytes and te...
Algorithms and Applications
This Festschrift volume, published to honor Esko Ukkonen on his 60th birthday, includes papers that present research on computational pattern matching and string algorithms, two areas that have benefited significantly from the work of Ukonen.
Natural Language and Information Systems
This book constitutes the refereed proceedings of the 13th International Conference on Applications of Natural Language to Information Systems, NLDB 2008, held in London, UK, in June 2008. The 31 revised full papers and 14 revised poster papers presented together with 3 invited talks and 4 papers of the NLDB 2008 doctoral symposium were carefully reviewed and selected from 82 submissions. The papers are organized in topical sections on natural language processing and understanding, conceptual modelling and ontologies, information retrieval, querying and question answering, document processing and text mining, software (requirements) engineering and specification.
Learning to Classify Text Using Support Vector Machines
Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications. Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning.
