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Inductive Logic Programming
"How often we recall, with regret", wrote Mark Twain about editors, "that Napoleon once shot at a magazine editor and missed him and killed a publisher. But we remember with charity, that his intentions were good. " Fortunately, we live in more forgiving times, and are openly able to express our pleasure at being the editors of this volume containing the papers selected for presentation at the 14th International Conference on Inductive Logic Programming. ILP 2004 was held in Porto from the 6th to the 8th of September, under the auspices of the Department of Electrical Engineering and Computing of the Faculty of Engineering of the University of Porto (FEUP), and the Laborat ́ orio de Intelig...
Inductive Logic Programming
This book constitutes the refereed proceedings of the 11th International Conference on Inductive Logic Programming, ILP 2001, held in Strasbourg, France in September 2001. The 21 revised full papers presented were carefully reviewed and selected from 37 submissions. Among the topics addressed are data mining issues for multi-relational databases, supervised learning, inductive inference, Bayesian reasoning, learning refinement operators, neural network learning, constraint satisfaction, genetic algorithms, statistical machine learning, transductive inference, etc.
Logic Programming
The Tenth International Conference on Logic Programming, sponsored by the Association for Logic Programming, is a major forum for presentations of research, applications, and implementations in this important area of computer science. Logic programming is one of the most promising steps toward declarative programming and forms the theoretical basis of the programming language Prolog and it svarious extensions. Logic programming is also fundamental to work in artificial intelligence, where it has been used for nonmonotonic and commonsense reasoning, expert systems implementation, deductive databases, and applications such as computer-aided manufacturing.David S. Warren is Professor of Computer Science at the State University of New York, Stony Brook.Topics covered: Theory and Foundations. Programming Methodologies and Tools. Meta and Higher-order Programming. Parallelism. Concurrency. Deductive Databases. Implementations and Architectures. Applications. Artificial Intelligence. Constraints. Partial Deduction. Bottom-Up Evaluation. Compilation Techniques.
Abductive Reasoning and Learning
This book contains leading survey papers on the various aspects of Abduction, both logical and numerical approaches. Abduction is central to all areas of applied reasoning, including artificial intelligence, philosophy of science, machine learning, data mining and decision theory, as well as logic itself.
Information Organization and Databases
Information Organization and Databases: Foundations of Data Organization provides recent developments of information organization technologies that have become crucial not only for data mining applications and information visualization, but also for treatment of semistructured data, spatio-temporal data and multimedia data that are not necessarily stored in conventional DBMSs. Information Organization and Databases: Foundations of Data Organization presents: semistructured data addressing XML, query languages and integrity constraints, focusing on advanced technologies for organizing web data for effective retrieval; multimedia database organization emphasizing video data organization and da...
Inductive Logic Programming
This book constitutes the thoroughly refereed post-conference proceedings of the 25th International Conference on Inductive Logic Programming, ILP 2015, held in Kyoto, Japan, in August 2015. The 14 revised papers presented were carefully reviewed and selected from 44 submissions. The papers focus on topics such as theories, algorithms, representations and languages, systems and applications of ILP, and cover all areas of learning in logic, relational learning, relational data mining, statistical relational learning, multi-relational data mining, relational reinforcement learning, graph mining, connections with other learning paradigms, among others.
