Detalhes do Documento

Improving Numerical Reasoning Capabilities of Inductive Logic Programming Systems

Autor(es): Alexessander Alves cv logo 1 ; Rui Camacho cv logo 2 ; Eugénio Oliveira cv logo 3

Data: 2004

Identificador Persistente: http://hdl.handle.net/10216/67382

Origem: Repositório Aberto da Universidade do Porto


Descrição
Inductive Logic Programming (ILP) systems have been largely applied to classification problems with a considerable success. The use of ILP systems in problems requiring numerical reasoning capabilities has been far less successful. Current systems have very limited numerical reasoning capabilities, which limits the range of domains where the ILP paradigm may be applied. This paper proposes improvements in numerical reasoning capabilities of ILP systems. It proposes the use of statistical-based techniques like Model Validation and Model Selection to improve noise handling and it introduces a new search stopping criterium based on the PAC method to evaluate learning performance. We have found these extensions essential to improve on results over statistical-based algorithms for time series forecasting used in the empirical evaluation study.
Tipo de Documento Documento de conferência
Idioma Inglês
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