
Inductive Logic Programming
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Content
- 6489
- Preface
- Organization
- Table of Contents
- Abstracts of Invited Talks
- Rule Interchange Format : Logic Programming's Second Wind?
- Practical Probabilistic Programming
- Introduction
- References
- Probabilistic Relational Learning and Inductive Logic Programming at a Global Scale
- References
- Research Papers
- Learning Multi-class Theories in ILP
- Introduction
- Motivation
- Improved Learning of Multi-class Theories
- Empirical Evaluation
- Related Work
- Concluding Remarks
- References
- A Numerical Refinement Operator Based on Multi-Instance Learning
- Introduction and Background
- Method
- Experiments
- Datasets
- Experimental Setup
- Experimental Results
- Conclusion
- References
- Not Far Away from Home: A Relational Distance-Based Approach to Understanding Images of Houses
- Introduction
- Setting
- Approach
- Experimental Setup and Results
- Conclusions
- References
- Approximate Inference for Logic Programs with Annotated Disjunctions
- Introduction
- Logic Programs with Annotated Disjunctions
- Approximate Inference
- k-best Algorithm
- Monte Carlo Algorithm
- Experiments
- References
- Approximate Bayesian Computation for the Parameters of PRISM Programs
- Introduction
- Approximate Bayesian Computation
- PRISM
- ABC for PRISM
- Choice of Prior Distribution
- Choice of Distance Function
- Choice of Perturbation Kernel
- Experimental Results
- Conclusions and Future Work
- References
- Probabilistic Rule Learning
- Introduction
- Problem Specification
- Analysis
- ProbFOIL: A Probabilistic First Order Rule Learner
- Experiments
- Surfing
- Eulisis
- Conclusions
- References
- Interactive Discriminative Mining of Chemical Fragments
- Introduction
- Background
- The iLogCHEM System
- Integrating Structural Information in the Search
- Macros
- Molecular Properties
- Interactive Search and Refinement
- Conclusions
- References
- MMRF for Proteome Annotation Applied to Human Protein Disease Prediction
- Introduction
- Modular Multi-Relational Framework
- MMRF Applied to Human Protein Disease Prediction
- Results and Discussion
- Conclusions and Further Work
- References
- Extending ProbLog with Continuous Distributions
- Introduction
- ProbLog
- Hybrid ProbLog
- Distribution Over Continuous Subprograms
- Success Probabilities of Queries
- Exact Inference
- Experiments
- Conclusions and Future Work
- References
- Multivariate Prediction for Learning on the Semantic Web
- Introduction
- Related Work
- Statistical Modeling
- Defining the Sample
- The Random Variables in the Data Matrix
- Non-random Covariates in the Data Matrix
- Algorithms for Learning with Statistical Units Node Sets
- Experiments
- Data Set and Experimental Setup
- Results
- Conclusions and Outlook
- References
- Learning Action Descriptions of Opponent Behaviour in the Robocup 2D Simulation Environment
- Introduction
- Action Descriptions for Dynamic Domains
- Efficient Induction of Action Laws with IAction
- Modeling Opponent Behaviour
- Discretization of the Environment
- Representation
- Learning Results
- Previous Work and Conclusions
- References
- Hypothesizing about Causal Networks with Positive and Negative Effects by Meta-level Abduction
- Introduction
- Meta-level Abduction
- Reasoning about Positive and Negative Causal Effects
- Alternating Axioms for Causality
- Axiomatization with Default Assumptions
- Case Study: p53 Signal Networks
- Enumerating Tumor Suppressors
- Recovering Links in CDK Networks
- Discussion and Related Work
- Conclusion
- References
- BET : An Inductive Logic Programming Workbench
- Introduction
- Overview
- Design of BET
- Need for Standard Format
- BET Standard Format
- Positive Example File:
- Negative Example File:
- Background knowledge File:
- Language Restriction File:
- Class Diagram and Components of BET
- Support for Building ILP Systems in BET
- Integrating a Legacy System in BET
- Implementing a New ILP System in BET
- Related Work
- Aleph
- GILPS
- Advantages of BET over Existing Systems
- Summing Up BET
- References
- Seeing the World through Homomorphism: An Experimental Study on Reducibility of Examples
- Introduction
- Preliminaries
- Reducing the Examples
- Experiments
- Mutagenesis
- Predictive Toxicology Challenge
- CAD
- Aleph with Reduced and Non-reduced Data
- Conclusions
- References
- Learning Discriminant Rules as a Minimal Saturation Search
- Introduction
- Preliminaries, Notations and Saturation
- Saturation of an Example
- Minimal Saturation to Search
- Refinement Operator
- Refinements Selection
- Learning a Rule
- Advantages and Limitations
- Application to Constraint Satisfaction Problem and Experiments
- Conclusion
- References
- Variation of Background Knowledge in an Industrial Application of ILP
- Introduction
- Application Descriptions
- University Innovation Centre (UIC) Overview
- Tomato Application
- Predictive Toxicology
- Ondex: A Biological Background Knowledge Generator
- Experiments
- Materials and Methods
- Results and Discussion
- Conclusions and Further Work
- References
- Pruning Search Space for Weighted First Order Horn Clause Satisfiability
- Introduction
- Satisfiability in Horn Clauses
- T Operator
- Modified_T Step
- Modified_Weighted_SAT
- Results
- Conclusion and Future Work
- References
- Multi-relational Pattern Mining Based-on Combination of Properties with Preserving Their Structure in Examples
- Introduction
- Patterns and Mapix Algorithm
- Ideas and an Algorithm
- Experiments and Concluding Remarks
- References
- Learning from Noisy Data Using a Non-covering ILP Algorithm
- Introduction
- Related Work
- HYPER/N
- Handling of Noisy Data in HYPER/N
- Learning Multiple Clauses from Noisy Data
- Some Additional Improvements
- Experiments with Weather Data
- Conclusion
- References
- Can HOLL Outperform FOLL?
- Introduction and Motivations
- ?Progol: A Higher-Order ILP System
- Results and Applications
- Future Works
- References
- Incremental Learning of Relational Action Models in Noisy Environments
- Introduction
- Learning Problem
- Action Model
- Relational Representation
- Matching and Covering
- Incremental Relational Learning of an Action Model
- Sketch of the Algorithm
- Conservative Generalizations and Specializations
- Covering and Elimination of Irrelevant Rules
- Prediction with the Model
- Empirical Study
- Conclusion
- References
- When Does It Pay Off to Use Sophisticated Entailment Engines in ILP?
- Introduction and Motivation
- The -Subsumption Problem
- Subsumption versus Resolution
- Entailment Algorithms in ProGolem
- Time Complexity
- Empirical Evaluation
- Materials and Methods
- Results and Discussion
- Conclusions and Future Directions
- References
- Stochastic Refinement
- Introduction
- Preliminaries
- Stochastic Refinement Operators
- Stochastic Refinement Search
- Stochastic Refinement Search as a Gibbs Sampling Algorithm
- Stochastic Refinement Search as a Random Heuristic Search
- Examples of Stochastic Refinement Search
- Stochastic Refinement Relative to a Bottom Clause
- Divisibility of the Subsumption Lattice Relative to ?
- Conclusions
- References
- Fire! Firing Inductive Rules from Economic Geography for Fire Risk Detection
- Introduction
- Related Work
- The Fire DataSet
- Methodology
- The Background Knowledge
- Results and Discussion
- Conclusions
- References
- Automating the ILP Setup Task : Converting User Advice about Specific Examples into General Background Knowledge
- Introduction
- The Onion
- Converting Advice to Background Knowledge
- Experiments
- Test Beds
- Results and Discussion
- Related Work
- Conclusions and Future Work
- References
- Speeding Up Planning through Minimal Generalizations of Partially Ordered Plans
- Introduction
- Method
- Experiments
- Conclusions
- References
- Author Index
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