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Abstract: In many real-world domains the task of machine learning algorithms is to learn a theory predicting numerical values. In particular several standard test domains used in Inductive Logic Programming (ILP) are concerned with predicting numerical values from examples and relational and mostly non-determinate background knowledge. However, so far no ILP algorithm except one can predict numbers and cope with non-determinate background knowledge. (The only exception is a covering algorithm called... (Update)
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BibTeX entry: (Update)
S. Kramer. Structural regression trees. In Proceedings of the 13th National Conference on Artificial Intelligence (AAAI-96), 1996. http://citeseer.ist.psu.edu/kramer96structural.html More
@inproceedings{ kramer96structural,
author = "S. Kramer",
title = "Structural Regression Trees",
booktitle = "Proceedings of the Thirteenth National Conference on Artificial Intelligence ({AAAI}-96)",
publisher = "AAAI Press/MIT Press",
address = "Cambridge/Menlo Park",
pages = "812--819",
year = "1996",
url = "citeseer.ist.psu.edu/kramer96structural.html" }
Citations (may not include all citations):2177 Programs for Machine Learning (context) - Quinlan - 1993
493 Modeling by shortest data description (context) - Rissanen - 1978
492 Learning logical definitions from relations (context) - Quinlan - 1990
271 Efficient induction of logic programs - Muggleton, Feng - 1992
190 The Wadsworth Statistics/Probability Series, Wadsworth Inter.. (context) - Breiman, Friedman et al. - 1984
139 Stochastic complexity and modeling (context) - Rissanen - 1986
57 Handling noise in Inductive Logic Programming (context) - Dzeroski, Bratko - 1992
48 Mutagenesis: ILP experiments in a non-determinate biological.. - Srinivasan, Muggleton et al. - 1994
45 Grammatically biased learning: Learning logic programs using.. (context) - Cohen - 1994
44 Learning with continuous classes - Quinlan - 1992
39 Learning in FOL with a similarity measure (context) - Bisson - 1992
37 Inductive Logic Programming (context) - Lavrac, Dzeroski - 1994
36 Rule-based machine learning methods for functional predictio.. - Weiss, Indurkhya - 1995
31 On finding the most probable model (context) - Cheeseman - 1990
31 Comparing the use of background knowledge by Inductive Logic.. (context) - Srinivasan, Muggleton et al. - 1995
27 First Order Regression (context) - Karalic - 1995
26 Employing linear regression in regression tree leaves - Karalic - 1992
18 Quantitative structureactivity relationships by neural netwo.. (context) - Hirst, King et al. - 1994
18 Quantitative structureactivity relationships by neural netwo.. (context) - Hirst, King et al. - 1994
14 Learning structural decision trees from examples (context) - Watanabe, Rendell - 1991
13 Compression-based evaluation of partial determinations - Pfahringer, Kramer - 1995
11 Finite element mesh design: An engineering domain for ILP ap.. (context) - Dolsak, Bratko et al. - 1994
11 Fossil: A robust relational learner (context) - Furnkranz - 1994
10 Relational cliches: Constraining constructive induction duri.. (context) - Silverstein, Pazzani - 1991
9 Covering vs. Divide-and-Conquer for Top-Down Induction of lo.. - Bostrom - 1995
9 Numerical Constraints and Learnability in Inductive Logic Pr.. (context) - Dzeroski - 1995
3 Knowledge-intensive induction (context) - Manago - 1989
3 Personal Communication (context) - Dzeroski, Kompare - 1995
2 Handling real numbers in inductive logic programming: A step.. (context) - Dzeroski, Todoroski et al. - 1995
2 A case study in machine learning - Quinlan - 1993
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