Inductive Logic Programming
Springer Berlin (Verlag)
978-3-642-38811-8 (ISBN)
A Relational Approach to Tool-Use Learning in Robots.- A Refinement Operator for Inducing Threaded-Variable Clauses.- Propositionalisation of Continuous Attributes beyond Simple Aggregation.- Topic Models with Relational Features for Drug Design.- Pairwise Markov Logic.- Evaluating Inference Algorithms for the Prolog Factor Language.- Polynomial Time Pattern Matching Algorithm for Ordered Graph Patterns.- Fast Parameter Learning for Markov Logic Networks Using Bayes Nets.- Bounded Least General Generalization.- Itemset-Based Variable Construction in Multi-relational Supervised Learning.- A Declarative Modeling Language for Concept Learning in Description Logics.- Identifying Driver's Cognitive Load Using Inductive Logic Programming.- Opening Doors: An Initial SRL Approach.- Probing the Space of Optimal Markov Logic Networks for Sequence Labeling.- What Kinds of Relational Features Are Useful for Statistical Learning?.- Learning Dishonesty.- Heuristic Inverse Subsumption in Full-Clausal Theories.- Learning Unordered Tree Contraction Patterns in Polynomial TimeA Relational Approach to Tool-Use Learning in Robots.- A Refinement Operator for Inducing Threaded-Variable Clauses.- Propositionalisation of Continuous Attributes beyond Simple Aggregation.- Topic Models with Relational Features for Drug Design.- Pairwise Markov Logic.- Evaluating Inference Algorithms for the Prolog Factor Language.- Polynomial Time Pattern Matching Algorithm for Ordered Graph Patterns.- Fast Parameter Learning for Markov Logic Networks Using Bayes Nets.- Bounded Least General Generalization.- Itemset-Based Variable Construction in Multi-relational Supervised Learning.- A Declarative Modeling Language for Concept Learning in Description Logics.- Identifying Driver's Cognitive Load Using Inductive Logic Programming.- Opening Doors: An Initial SRL Approach.- Probing the Space of Optimal Markov Logic Networks for Sequence Labeling.- What Kinds of Relational Features Are Useful for StatisticalLearning?.-Learning Dishonesty.-Heuristic Inverse Subsumption in Full-Clausal Theories.-Learning Unordered Tree Contraction Patterns in Polynomial Time.
Erscheint lt. Verlag | 6.6.2013 |
---|---|
Reihe/Serie | Lecture Notes in Artificial Intelligence | Lecture Notes in Computer Science |
Zusatzinfo | X, 273 p. 81 illus. |
Verlagsort | Berlin |
Sprache | englisch |
Maße | 155 x 235 mm |
Gewicht | 434 g |
Themenwelt | Informatik ► Software Entwicklung ► User Interfaces (HCI) |
Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
Schlagworte | algorithms • Markov logic networks • multi-relational learning • Robotics • structured data |
ISBN-10 | 3-642-38811-6 / 3642388116 |
ISBN-13 | 978-3-642-38811-8 / 9783642388118 |
Zustand | Neuware |
Haben Sie eine Frage zum Produkt? |
aus dem Bereich