When using Horn Clause Logic as a representation formalism, the use of uninterpreted predicates cannot fully account for the complexity of some domains. In particular, in Machine Learning frameworks based on Horn Clause Logic, purely syntactic generalization cannot be applied to these kinds of predicates, requiring specific problems to be addressed and tailored strategies and techniques to be introduced. Among others, outstanding examples are those of numeric, taxonomic or sequential information. This paper deals with the case of (multidimensional) sequential information.Coverage and generalization techniques are devised and presented, and their integration in an incremental ILP system is used to run experiments showing its performance.
A Heuristic Approach to Handling Sequential Information in Incremental ILP
FERILLI, Stefano;ESPOSITO, Floriana
2013-01-01
Abstract
When using Horn Clause Logic as a representation formalism, the use of uninterpreted predicates cannot fully account for the complexity of some domains. In particular, in Machine Learning frameworks based on Horn Clause Logic, purely syntactic generalization cannot be applied to these kinds of predicates, requiring specific problems to be addressed and tailored strategies and techniques to be introduced. Among others, outstanding examples are those of numeric, taxonomic or sequential information. This paper deals with the case of (multidimensional) sequential information.Coverage and generalization techniques are devised and presented, and their integration in an incremental ILP system is used to run experiments showing its performance.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
