Probabilistic Inductive Logic Programming  [electronic resource] / 
edited by Luc De Raedt, Paolo Frasconi, Kristian Kersting, Stephen H. Muggleton. 
 - 1st ed. 2008. 
 - VIII, 341 p.  online resource. 
 - Lecture Notes in Artificial Intelligence,  4911  2945-9141 ; .
 - Lecture Notes in Artificial Intelligence,  4911 .
<br/><br/>Probabilistic Inductive Logic Programming -- Formalisms and Systems -- Relational Sequence Learning -- Learning with Kernels and Logical Representations -- Markov Logic -- New Advances in Logic-Based Probabilistic Modeling by PRISM -- CLP( ): Constraint Logic Programming for Probabilistic Knowledge -- Basic Principles of Learning Bayesian Logic Programs -- The Independent Choice Logic and Beyond -- Applications -- Protein Fold Discovery Using Stochastic Logic Programs -- Probabilistic Logic Learning from Haplotype Data -- Model Revision from Temporal Logic Properties in Computational Systems Biology -- Theory -- A Behavioral Comparison of Some Probabilistic Logic Models -- Model-Theoretic Expressivity Analysis. 
<br/><br/><label>ISBN: </label>9783540786528 
<br/><br/><label>Standard No.: </label>10.1007/978-3-540-78652-8  doi 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Artificial intelligence.<br/>Computer programming.<br/>Machine theory.<br/>Algorithms.<br/>Data mining.<br/>Bioinformatics.<br/>Artificial Intelligence.<br/>Programming Techniques.<br/>Formal Languages and Automata Theory.<br/>Algorithms.<br/>Data Mining and Knowledge Discovery.<br/>Computational and Systems Biology.
<br/><br/><label>LC Class. No.: </label>Q334-342 TA347.A78 
<br/><br/><label>Dewey Class. No.: </label>006.3