FORS
swMATH ID:  4848 
Software Authors:  Karalić, Aram; Bratko, Ivan 
Description:  A new approach developed in ILP, called First Order Regression (FOR), is a combination of ILP and numerical regression. Firstorder logic descriptions are induced to carve out those subspaces that are amenable to numerical regression among realvalued 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 positiveonly and negativeonly examples. 
Homepage:  http://wwwai.ijs.si/~ilpnet2/systems/fors.html 
Programming Languages:  C mixed with SICStus Prolog 
Operating Systems:  None 
Dependencies:  None 
Keywords:  firstorder regression; inductive logic programming 
Related Software:  UCIml; C4.5; GOLEM; Aleph; shap; JDQZ; LIBLINEAR; JDQR; ReliefF; MALSAR; MULAN; Orange4WS; nFOIL; gBoost; kFOIL; Pegasos; FOIL 
Referenced in:  13 Publications 
Standard Articles
1 Publication describing the Software, including 1 Publication in zbMATH  Year 

First order regression. Zbl 0866.68089 Karalić, Aram; Bratko, Ivan 
1997

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Referenced by 32 Authors
Referenced in 5 Serials
5  Machine Learning 
1  Artificial Intelligence 
1  Optimization Methods & Software 
1  Journal of Machine Learning Research (JMLR) 
1  Electronic Journal of Statistics 
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