Del i serien Lecture Notes in Computer Science

Inductive Logic Programming

22nd International Conference, ILP 2012, Dubrovnik, Croatia, September 16-18,2012, Revised Selected papers

Häftad, Engelska, 2013

689 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt för medlemmar vid köp för minst 249 kr.

This book constitutes the thoroughly refereed post-proceedings of the 22nd International Conference on Inductive Logic Programming, ILP 2012, held in Dubrovnik, Croatia, in September 2012. The 18 revised full papers were carefully reviewed and selected from 41 submissions. The papers cover the following topics: propositionalization, logical foundations, implementations, probabilistic ILP, applications in robotics and biology, grammatical inference, spatial learning and graph-based learning.

Produktinformation

Utgivningsdatum2013-06-06
Mått155 x 235 x 16 mm
Vikt435 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor273
Upplaga2013
FörlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN9783642388118

Tillhör följande kategorier

Systemvetenskap och AI inom Data och IT
Programmeringsböcker inom Data och IT
Artificiell intelligens inom Data och IT
Människa – datorinteraktion inom Data och IT

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.

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Del i serien Lecture Notes in Computer Science

Inductive Logic Programming

22nd International Conference, ILP 2012, Dubrovnik, Croatia, September 16-18,2012, Revised Selected papers

Häftad, Engelska, 2013

AvFabrizio Riguzzi,Filip Zelezny

689 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt för medlemmar vid köp för minst 249 kr.

Produktinformation

Utgivningsdatum2013-06-06
Mått155 x 235 x 16 mm
Vikt435 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor273
Upplaga2013
FörlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN9783642388118

Tillhör följande kategorier

Systemvetenskap och AI inom Data och IT
Programmeringsböcker inom Data och IT
Artificiell intelligens inom Data och IT
Människa – datorinteraktion inom Data och IT

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.