Inductive Logic Programming  17th International Conference, ILP 2007, Corvallis, OR, USA, June 19-21, 2007, Revised Selected Papers /  [electronic resource] : 
edited by Hendrik Blockeel, Jan Ramon, Jude Shavlik, Prasad Tadepalli. 
 - 1st ed. 2008. 
 - XI, 307 p.  online resource. 
 - Lecture Notes in Artificial Intelligence,  4894  2945-9141 ; .
 - Lecture Notes in Artificial Intelligence,  4894 .
<br/><br/>Invited Talks -- Learning with Kernels and Logical Representations -- Beyond Prediction: Directions for Probabilistic and Relational Learning -- Extended Abstracts -- Learning Probabilistic Logic Models from Probabilistic Examples (Extended Abstract) -- Learning Directed Probabilistic Logical Models Using Ordering-Search -- Learning to Assign Degrees of Belief in Relational Domains -- Bias/Variance Analysis for Relational Domains -- Full Papers -- Induction of Optimal Semantic Semi-distances for Clausal Knowledge Bases -- Clustering Relational Data Based on Randomized Propositionalization -- Structural Statistical Software Testing with Active Learning in a Graph -- Learning Declarative Bias -- ILP :- Just Trie It -- Learning Relational Options for Inductive Transfer in Relational Reinforcement Learning -- Empirical Comparison of "Hard" and "Soft" Label Propagation for Relational Classification -- A Phase Transition-Based Perspective on Multiple Instance Kernels -- Combining Clauses with Various Precisions and Recalls to Produce Accurate Probabilistic Estimates -- Applying Inductive Logic Programming to Process Mining -- A Refinement Operator Based Learning Algorithm for the Description Logic -- Foundations of Refinement Operators for Description Logics -- A Relational Hierarchical Model for Decision-Theoretic Assistance -- Using Bayesian Networks to Direct Stochastic Search in Inductive Logic Programming -- Revising First-Order Logic Theories from Examples Through Stochastic Local Search -- Using ILP to Construct Features for Information Extraction from Semi-structured Text -- Mode-Directed Inverse Entailment for Full Clausal Theories -- Mining of Frequent Block Preserving Outerplanar Graph Structured Patterns -- Relational Macros for Transfer in Reinforcement Learning -- Seeing theForest Through the Trees -- Building Relational World Models for Reinforcement Learning -- An Inductive Learning System for XML Documents. 
<br/><br/><label>ISBN: </label>9783540784692 
<br/><br/><label>Standard No.: </label>10.1007/978-3-540-78469-2  doi 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Artificial intelligence.<br/>Software engineering.<br/>Computer programming.<br/>Machine theory.<br/>Algorithms.<br/>Data mining.<br/>Artificial Intelligence.<br/>Software Engineering.<br/>Programming Techniques.<br/>Formal Languages and Automata Theory.<br/>Algorithms.<br/>Data Mining and Knowledge Discovery.
<br/><br/><label>LC Class. No.: </label>Q334-342 TA347.A78 
<br/><br/><label>Dewey Class. No.: </label>006.3