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Additive and Cancellative Interacting Particle Systems (Lecture Notes in Mathematics) by David Griffeath (Repost)
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Mathematics Probability, Markov Chains, Queues, and Simulation - The Mathematical Basis of Performance Modeling
Algebraic Aspects of Cryptography (Algorithms and Computation in Mathematics) by Neal Koblitz (Repost)
Mathematics Mathematical Foundations of Computer Science 2004 [Repost]
Mathematics Mathematical Logic for Computer Science (3rd edition)
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-Mathematics for the Physical Sciences- by Herbert S. Wilf
Mathematics for Elementary Teachers - A Conceptual Approach, 9 edition
Computer-Enabled Mathematics - Integrating Experiment and Theory in Teacher Education
How to Fold It - The Mathematics of Linkages, Origami and Polyhedra
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Mathematics Maverick Mathematician: The Life and Science of J.E. Moyal
African Mathematics: From Bones to Computers (repost)
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Mathematics An Inductive Logic Programming Approach to Statistical Relational Learning (Frontiers in Artificial Intelligence and Applications, Vol. 148) (Frontiers in Artificial Intelligence and Applications)
Posted on 2010-04-15
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Author: K. Kersting
Publisher: IOS Press (2006) Binding: Hardcover, 256 pages pricer: $131.00 ISBN-10: 1586036742 editorialreviews In this publication, the author Kristian Kersting has made an assault on one of the hardest integration problems at the heart of Artificial Intelligence research. This involves taking three disparate major areas of research and attempting a fusion among them. The three areas are: Logic Programming, Uncertainty Reasoning and Machine Learning. Every one of these is a major sub-area of research with its own associated international research conferences. Having taken on such a Herculean task, Kersting has produced a series of results which are now at the core of a newly emerging area: Probabilistic Inductive Logic Programming. The new area is closely tied to, though strictly subsumes, a new field known as ¡®Statistical Relational Learning¡¯ which has in the last few years gained major prominence in the American Artificial Intelligence research community. Within this book, the author makes several major contributions, including the introduction of a series of definitions which circumscribe the new area formed by extending Inductive Logic Programming to the case in which clauses are annotated with probability values. Also, Kersting investigates the approach of Learning from proofs and the issue of upgrading Fisher Kernels to Relational Fisher Kernels. checked checked checked checked failed
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