PFORTE: Revising probabilistic FOL theories

• Type: Article in International Conference Proceedings Book
• Date: 2006

Authors

Paes, A (Author) Other; Revoredo, K (Author) Other; Zaverucha, G (Author) Other; Costa, VS (Author) FCUP

Conference proceedings International

Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Pages: 441-450

IBERAMIA-SBIA 2006 - 2nd International Joint Conference, 10th Ibero-American Conference on AI, 18th Brazilian AI Symposium

Ribeirao Preto, 23 October 2006 through 27 October 2006

Indexing

Scopus - 3 Citations

Other information

Authenticus ID: P-007-GMP

DOI: 10.1007/11874850_48

Abstract (EN): There has been significant recent progress in the integration of probabilistic reasoning with first order logic representations (SRL). So far, the learning algorithms developed for these models all learn from scratch, assuming an invariant background knowledge. As an alternative, theory revision techniques have been shown to perform well on a variety of machine learning problems. These techniques start from an approximate initial theory and apply modifications in places that performed badly in classification. In this work we describe the first revision system for SRL classification, PFORTE, which addresses two problems: all examples must be classified, and they must be classified well. PFORTE uses a two step-approach. The completeness component uses generalization operators to address failed proofs and the classification component addresses classification problems using generalization and specialization operators. Experimental results show significant benefits from using theory revision techniques compared to learning from scratch. © Springer-Verlag Berlin Heidelberg 2006.

Language: English

Type (Professor's evaluation): Scientific