FORS

A new approach developed in ILP, called First Order Regression (FOR), is a combination of ILP and numerical regression. First-order logic descriptions are induced to carve out those subspaces that are amenable to numerical regression among real-valued variables. The program FORS (First Order Regression System) is an implementation of this idea, where numerical regression is focused on a distinguished continuous argument of the target predicate. This can be viewed as a generalisation of the usual ILP problem. Namely, the target predicate in usual ILP can be modified by adding an extra ”continuous” attribute whose value would be determined by the truth of the examples: 1.0 for positive examples and 0.0 for negative. The regression formulas would only involve this attribute and FORS would tend to find rules that cover subsets of positive-only and negative-only examples.


References in zbMATH (referenced in 13 articles , 1 standard article )

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  1. Panov, Panče; Soldatova, Larisa; Džeroski, Sašo: Ontology of core data mining entities (2014)
  2. Landwehr, Niels; Passerini, Andrea; De Raedt, Luc; Frasconi, Paolo: Fast learning of relational kernels (2010)
  3. Passerini, Andrea; Frasconi, Paolo; De Raedt, Luc: Kernels on Prolog proof trees: statistical learning in the ILP setting (2006)
  4. Srinivasan, Ashwin; Page, David; Camacho, Rui; King, Ross: Quantitative pharmacophore models with inductive logic programming (2006)
  5. Srinivasan, Ashwin; Page, David; Camacho, Rui; King, Ross: Quantitative pharmacophore models with inductive logic programming (2006)
  6. Flach, Peter; Lavrac, Nada: Learning in clausal logic: A perspective on inductive logic programming (2002)
  7. Hoche, Susanne; Wrobel, Stefan: Relational learning using constrained confidence-rated boosting (2001)
  8. Džeroski, Sašo; Cussens, James; Manandhar, Suresh: An introduction to inductive logic programming and learning language in logic (2000)
  9. Lavrac, Nada; Dzeroski, Saso; Numao, Masayuki: Inductive logic programming for relational knowledge discovery. (1999)
  10. Lavrac, Nada; Dzeroski, Saso; Numao, Masayuki: Inductive logic programming for relational knowledge discovery. (1999)
  11. Blockeel, Hendrik; de Raedt, Luc: Top-down induction of first-order logical decision trees (1998)
  12. Lavrač, Nada: Inductive logic programming for relational knowledge discovery (1998)
  13. Karalić, Aram; Bratko, Ivan: First order regression (1997)