Learning Logic Formulas from Data

Relatore
Klaus Truemper - University of Texas at Dallas
Data e ora
martedì 15 giugno 2004 alle ore 17.00
Luogo
Ca' Vignal - Piramide, Piano 0, Sala Verde
Referente
Maria Paola Bonacina
Referente esterno
Data pubblicazione
22 aprile 2004
Dipartimento
 

Riassunto

Among the important tools for the formulation of logic-based intelligent systems are methods that extract logic formulas from data. For example, for systems handling credit rating, medical diagnosis, or natural language processing, such extraction methods are very useful. In this talk we present one such method, called Lsquare, for the extraction task. Lsquare supports the derivation of shortest, longest, and optimized formulas. The method requires that the data contain only True/False values. Often, that condition is not satisfied. For example, some values may be rational numbers or elements of finite, but possibly large, sets. We describe a second method, called Cutpoint, that transforms such values to True/False values to which Lsquare can be applied. Finally, we cover some computational results that prove the two methods to be effective and useful.





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