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. The goal of the method is to find descriptions which are general (in other words, are successfully applicable to recognize objects different from the ones in the training set), preserving at the same time their discrimination ability. This method has been applied to predictive toxicology evaluation, that is the inference of the cancerogenic characteristics of chemical compounds.
|Titolo:||Learning Graphs from examples: an application to the prediction of the toxicity of chemical compounds|
|Autori interni:||FOGGIA, PASQUALE|
|Data di pubblicazione:||2006|
|Rivista:||INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE|
|Appare nelle tipologie:||1.1.2 Articolo su rivista con ISSN|