BoltzCONS: dynamic symbol structures in a connectionist network
Artificial Intelligence - On connectionist symbol processing
Mapping part-whole hierarchies into connectionist networks
Artificial Intelligence - On connectionist symbol processing
Shape recognition by integrating structural descriptions and geometrical/statistical transforms
Computer Vision and Image Understanding
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
A Shape Analysis Model with Applications to a Character Recognition System
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Learning Structural Descriptions From Examples
Learning Structural Descriptions From Examples
A general framework for adaptive processing of data structures
IEEE Transactions on Neural Networks
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In this paper, a novel algorithm for learning structured descriptions, ascribable to the category of symbolic techniques, is proposed. It faces the problem directly in the space of the graphs, by defining the proper inference operators, as graph generalization and graph specialization, and obtains general and coherent prototypes with a low computational cost with respect to other symbolic learning systems. The proposed algorithm is tested with reference to a problem of handwritten character recognition from a standard database.