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Scientific Computing and Applications
Scientific Computing & Applications
Numerical Solution of Nonlinear Elliptic Problems Via Preconditioning Operators
Numerical Solution of Nonlinear Elliptic Problems Via Preconditioning Operators - Theory & Applications
Foundations of Information and Knowledge Systems
This book constitutes the refereed proceedings of the 10th International Symposium on Foundations of Information and Knowledge Systems, FoIKS 2018, held in Budapest, Hungary, in May 2018.The 20 revised full papers presented together with 1 invited talk were carefully reviewed and selected from 40 submissions. The papers address various topics such as big data; database design; dynamics of information; information fusion; integrity and constraint management; intelligent agents; knowledge discovery and information retrieval; knowledge representation, reasoning and planning; logics in databases and AI; mathematical foundations; security in information and knowledge systems; semi-structured data and XML; social computing; the semantic web and knowledge management; and the world wide web.
Automata, Languages and Programming
The two-volume set LNCS 4051 and LNCS 4052 constitutes the refereed proceedings of the 33rd International Colloquium on Automata, Languages and Programming, ICALP 2006, held in Venice, Italy, July 2006. In all, these volumes present more 100 papers and lectures. Volume I (4051) presents 61 revised full papers together with 1 invited lecture, focusing on algorithms, automata, complexity and games, on topics including graph theory, quantum computing, and more.
Combinatorial Geometry
A complete, self-contained introduction to a powerful and resurgingmathematical discipline . Combinatorial Geometry presents andexplains with complete proofs some of the most important resultsand methods of this relatively young mathematical discipline,started by Minkowski, Fejes Toth, Rogers, and Erd???s. Nearly halfthe results presented in this book were discovered over the pasttwenty years, and most have never before appeared in any monograph.Combinatorial Geometry will be of particular interest tomathematicians, computer scientists, physicists, and materialsscientists interested in computational geometry, robotics, sceneanalysis, and computer-aided design. It is also a superb textbook,complete with end-of-chapter problems and hints to their solutionsthat help students clarify their understanding and test theirmastery of the material. Topics covered include: * Geometric number theory * Packing and covering with congruent convex disks * Extremal graph and hypergraph theory * Distribution of distances among finitely many points * Epsilon-nets and Vapnik--Chervonenkis dimension * Geometric graph theory * Geometric discrepancy theory * And much more
Algorithmic Learning Theory
This book constitutes the refereed proceedings of the 16th International Conference on Algorithmic Learning Theory, ALT 2005, held in Singapore in October 2005. The 30 revised full papers presented together with 5 invited papers and an introduction by the editors were carefully reviewed and selected from 98 submissions. The papers are organized in topical sections on kernel-based learning, bayesian and statistical models, PAilearning, query-learning, inductive inference, language learning, learning and logic, learning from expert advice, online learning, defensive forecasting, and teaching.
Learning Theory and Kernel Machines
This book constitutes the joint refereed proceedings of the 16th Annual Conference on Computational Learning Theory, COLT 2003, and the 7th Kernel Workshop, Kernel 2003, held in Washington, DC in August 2003. The 47 revised full papers presented together with 5 invited contributions and 8 open problem statements were carefully reviewed and selected from 92 submissions. The papers are organized in topical sections on kernel machines, statistical learning theory, online learning, other approaches, and inductive inference learning.
Theoretical Advances in Neural Computation and Learning
For any research field to have a lasting impact, there must be a firm theoretical foundation. Neural networks research is no exception. Some of the founda tional concepts, established several decades ago, led to the early promise of developing machines exhibiting intelligence. The motivation for studying such machines comes from the fact that the brain is far more efficient in visual processing and speech recognition than existing computers. Undoubtedly, neu robiological systems employ very different computational principles. The study of artificial neural networks aims at understanding these computational prin ciples and applying them in the solutions of engineering problems. Due to the rec...
Machines that Learn to Play Games
The mind-set that has dominated the history of computer game playing relies on straightforward exploitation of the available computing power. The fact that a machine can explore millions of variations sooner than the sluggish human can wink an eye has inspired hopes that the mystery of intelligence can be cracked, or at least side-stepped, by sheer force. Decades of the steadily growing strength of computer programs have attested to the soundness of this approach. It is clear that deeper understanding can cut the amount of necessary calculations by orders of magnitude. The papers collected in this volume describe how to instill learning skills in game playing machines. The reader is asked to keep in mind that this is not just about games -- the possibility that the discussed techniques will be used in control systems and in decision support always looms in the background.
