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Bioinformatics
"There are fundamental principles for problem analysis and algorithm design that are continuously used in bioinformatics. This book concentrates on a clear presentation of these principles, presenting them in a self-contained, mathematically clear and precise manner, and illustrating them with lots of case studies from main fields of bioinformatics. Emphasis is laid on algorithmic "pearls" of bioinformatics, showing that things may get rather simple when taking a proper view into them. The book closes with a thorough bibliography, ranging from classic research results to very recent findings, providing many pointers for future research. Overall, this volume is ideally suited for a senior undergraduate or graduate course on bioinformatics, with a strong focus on its mathematical and computer science background."--BOOK JACKET.
New Trends in Constraints
This book constitutes the thoroughly refereed post-proceedings of the Joint ERCIM/Compulog-Net Workshop on New Trends in Constraints held in Paphos, Cyprus, Greece in October 1999. The 12 revised full research papers presented together with four surveys by leading researchers were carefully reviewed. The book is divided in topical sections on constraint propagation and manipulation, constraint programming, and rule-based constraint programming.
Hierarchical Neural Networks for Image Interpretation
Human performance in visual perception by far exceeds the performance of contemporary computer vision systems. While humans are able to perceive their environment almost instantly and reliably under a wide range of conditions, computer vision systems work well only under controlled conditions in limited domains. This book sets out to reproduce the robustness and speed of human perception by proposing a hierarchical neural network architecture for iterative image interpretation. The proposed architecture can be trained using unsupervised and supervised learning techniques. Applications of the proposed architecture are illustrated using small networks. Furthermore, several larger networks were trained to perform various nontrivial computer vision tasks.
Computation Theory and Logic
In Memory of Dieter Rötting. 24.8.1937 - 4.6.1984. On the Occasion of the 50th Anniversary of His Birth
Nonmonotonic Reasoning
Nonmonotonic reasoning provides formal methods that enable intelligent systems to operate adequately when faced with incomplete or changing information. In particular, it provides rigorous mechanisms for taking back conclusions that, in the presence of new information, turn out to be wrong and for deriving new, alternative conclusions instead. Nonmonotonic reasoning methods provide rigor similar to that of classical reasoning; they form a base for validation and verification and therefore increase confidence in intelligent systems that work with incomplete and changing information. Following a brief introduction to the concepts of predicate logic that are needed in the subsequent chapters, this book presents an in depth treatment of default logic. Other subjects covered include the major approaches of autoepistemic logic and circumscription, belief revision and its relationship to nonmonotonic inference, and briefly, the stable and well-founded semantics of logic programs.
Learning with Recurrent Neural Networks
Folding networks, a generalisation of recurrent neural networks to tree structured inputs, are investigated as a mechanism to learn regularities on classical symbolic data, for example. The architecture, the training mechanism, and several applications in different areas are explained. Afterwards a theoretical foundation, proving that the approach is appropriate as a learning mechanism in principle, is presented: Their universal approximation ability is investigated- including several new results for standard recurrent neural networks such as explicit bounds on the required number of neurons and the super Turing capability of sigmoidal recurrent networks. The information theoretical learnabi...
Scandinavian Conference on Artificial Intelligence-93
The Nordic countries are traditionally strong in the application of new computer technology. This work represents the state of the art in some of the artificial intelligence areas currently subject to active R&D. International researchers present 24 papers in the following areas: automated reasoning; qualitative reasoning; machine learning; neural networks; and knowledge-based systems. Both basic and applied AI research are represented.
Logic Programming and Automated Reasoning
LPAR is an international conference series aimed at bringing together researchers interested in logic programming and automated reasoning. The research in logic programming grew out of the research in automated reasoning in the early 1970s. Later, the implementation techniques known from logic programming were used in implementing theorem proving systems. Results from both fields applied to deductive databases. This volume contains the proceedings of LPAR '93, which was organized by the Russian Association for Logic Programming. The volume contains 35 contributed papers selected from 84 submissions, together with an invited paper by Peter Wegner entitled "Reasoning versus modeling in computer science".
