Petri nets: an introduction
Handbook of graph grammars and computing by graph transformation: volume I. foundations
Handbook of graph grammars and computing by graph transformation: volume I. foundations
Machine Learning
Intelligent Data Analysis: An Introduction
Intelligent Data Analysis: An Introduction
Soft Computing and Fuzzy Logic
IEEE Software
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
IEEE Transactions on Fuzzy Systems
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
Hi-index | 0.00 |
Different learning algorithms based on learning from examples are described based on a set of graph rewrite rules. Starting from either a very general or a very special rule set which is modeled as graph, two to three basic rewrite rules are applied until a rule graph explaining all examples is reached. The rewrite rules can also be used to model the corresponding hypothesis space as they describe partial relations between different rule set graphs. The possible paths, algorithms can take through the hypothesis space can be described as application sequences. This schema is applied to general learning algorithms as well as to fuzzy rule learning algorithms.