Librería Portfolio

This book provides an introduction to probabilistic inductive logic programming. It places emphasis on the methods based on logic programming principles and covers formalisms and systems, implementations and applications, as well as theory.



Table of Contents:
Introduction -- Probabilistic Inductive Logic Programming / Luc De Raedt, Kristian Kersting -- Formalisms and Systems -- Relational Sequence Learning / Kristian Kersting, Luc De Raedt, Bernd Gutmann, Andreas Karwath, Niels Landwehr -- Learning with Kernels and Logical Representations / Paolo Frasconi, Andrea Passerini -- Markov Logic / Pedro Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, Parag Singla -- New Advances in Logic-Based Probabilistic Modeling by PRISM / Taisuke Sato, Yoshitaka Kameya -- CLP(BN): Constraint Logic Programming for Probabilistic Knowledge / Vitor Santos Costa, David Page, James Cussens -- Basic Principles of Learning Bayesian Logic Programs / Kristian Kersting, Luc De Raedt -- The Independent Choice Logic and Beyond / David Poole -- Applications -- Protein Fold Discovery Using Stochastic Logic Programs / Jianzhong Chen, Lawrence Kelley, Stephen Muggleton, Michael Sternberg -- Probabilistic Logic Learning from Haplotype Data / Niels Landwehr, Taneli Mielikainen -- Model Revision from Temporal Logic Properties in Computational Systems Biology / Francois Fages, Sylvain Soliman -- Theory -- A Behavioral Comparison of Some Probabilistic Logic Models / Stephen Muggleton, Jianzhong Chen -- Model-Theoretic Expressivity Analysis / Manfred Jaeger -- Author Index.