An Image Understanding System Using Attributed Symbolic Representation and Inexact Graph-Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mapping part-whole hierarchies into connectionist networks
Artificial Intelligence - On connectionist symbol processing
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Symbolic vs. Connectionist Learning: An Experimental Comparison in a Structured Domain
IEEE Transactions on Knowledge and Data Engineering
Learning Logical Definitions from Relations
Machine Learning
Prototyping Structural Descriptions: An Inductive Learning Approach
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
Learning Structural Descriptions From Examples
Learning Structural Descriptions From Examples
Substructure discovery using minimum description length and background knowledge
Journal of Artificial Intelligence Research
Pattern Recognition as Rule-Guided Inductive Inference
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A common problem encountered in structural pattern recognition is the difficulty of constructing classification models or rules from a set of examples, due to the complexity of the structures needed to represent the patterns. In this paper we present an extension of a method for structural learning applied to predictive toxicology evaluation.