1.

Record Nr.

UNISA996466245703316

Titolo

Inductive Logic Programming [[electronic resource] ] : 17th International Conference, ILP 2007, Corvallis, OR, USA, June 19-21, 2007,  Revised Selected Papers / / edited by Hendrik Blockeel, Jan Ramon, Jude Shavlik, Prasad Tadepalli

Pubbl/distr/stampa

Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2008

ISBN

3-540-78469-1

Edizione

[1st ed. 2008.]

Descrizione fisica

1 online resource (XI, 307 p.)

Collana

Lecture Notes in Artificial Intelligence ; ; 4894

Disciplina

005.1/5

Soggetti

Artificial intelligence

Software engineering

Computer programming

Mathematical logic

Algorithms

Data mining

Artificial Intelligence

Software Engineering/Programming and Operating Systems

Programming Techniques

Mathematical Logic and Formal Languages

Algorithm Analysis and Problem Complexity

Data Mining and Knowledge Discovery

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Bibliographic Level Mode of Issuance: Monograph

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

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 the Forest Through the Trees -- Building Relational World Models for Reinforcement Learning -- An Inductive Learning System for XML Documents.