Learning from hints in neural networks
Journal of Complexity
An analysis of first-order logics of probability
Artificial Intelligence
Proceedings of the sixth international workshop on Machine learning
Deduction in top-down inductive learning
Proceedings of the sixth international workshop on Machine learning
One-sided algorithms for integrating empirical and explanation-based learning
Proceedings of the sixth international workshop on Machine learning
Finding new rules for incomplete theories: explicit biases for induction with contextual information
Proceedings of the sixth international workshop on Machine learning
Explanation-based learning with weak domain theories
Proceedings of the sixth international workshop on Machine learning
Integrating learning in a neural network
Proceedings of the sixth international workshop on Machine learning
Combining explanation-based learning and artificial neural networks
Proceedings of the sixth international workshop on Machine learning
Comparison of empirical and computed values of fuzzy conjunction
Fuzzy Sets and Systems
The Utility of Knowledge in Inductive Learning
Machine Learning
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Applications of inductive logic programming
Communications of the ACM
Inductive logic programming and knowledge discovery in databases
Advances in knowledge discovery and data mining
Scientific knowledge discovery using inductive logic programming
Communications of the ACM
Knowledge discovery based on neural networks
Communications of the ACM
Data mining in finance: advances in relational and hybrid methods
Data mining in finance: advances in relational and hybrid methods
Fundamental Statistics for Social Research: Step-by-Step Calculations and Computer Techniques Using SPSS for Windows
Creating a Memory of Causal Relationships: An Integration of Empirical and Explanation-Based Learning Methods
Machine Learning
Learning Logical Definitions from Relations
Machine Learning
A Study of Explanation-Based Methods for Inductive Learning
Machine Learning
Strongly Typed Inductive Concept Learning
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Learning probabilities for noisy first-order rules
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Intelligent Data Analysis - New Methods in Bioinformatics Presented at the Fifth International Conference on Bioinformatics of Genome Regulation and Structure
Intelligent Data Analysis - New Methods in Bioinformatics Presented at the Fifth International Conference on Bioinformatics of Genome Regulation and Structure
KONT'07/KPP'07 Proceedings of the First international conference on Knowledge processing and data analysis
Probabilistic concepts in formal contexts
PSI'11 Proceedings of the 8th international conference on Perspectives of System Informatics
Probabilistic generalization of formal concepts
Programming and Computing Software
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The purpose of this work is to analyse the cognitive process of the domain theories in terms of the measurement theory to develop a computational machine learning approach for implementing it. As a result, the relational data mining approach, the authors proposed in the preceding books, was improved. We present the approach as an implementation of the cognitive process as the measurement theory perceived. We analyse the cognitive process in the first part of the paper and present the theory and method of the logically most powerful empirical theory discovery in the second. The theory is based on the notion of 'law-like' rules, which conform to all the properties of laws of nature, namely generality, simplicity, maximum refutability and minimum number of parameters. This notion is defined for deterministic and probabilistic cases. Based on the method, the 'discovery' system is developed. The system was successfully applied to many practical tasks.