- New
-
Topics
- All Categories
- Metaphysics and Epistemology
- Value Theory
- Science, Logic, and Mathematics
- Science, Logic, and Mathematics
- Logic and Philosophy of Logic
- Philosophy of Biology
- Philosophy of Cognitive Science
- Philosophy of Computing and Information
- Philosophy of Mathematics
- Philosophy of Physical Science
- Philosophy of Social Science
- Philosophy of Probability
- General Philosophy of Science
- Philosophy of Science, Misc
- History of Western Philosophy
- Philosophical Traditions
- Philosophy, Misc
- Other Academic Areas
- Journals
- Submit material
- More
Top-down induction of first-order logical decision trees
Artificial Intelligence 101 (1-2):285-297 (1998)
Abstract
This article has no associated abstract. (fix it)Author's Profile
My notes
Similar books and articles
Theories for mutagenicity: a study in first-order and feature-based induction.A. Srinivasan, S. H. Muggieton, M. J. E. Sternberg & R. D. King - 1996 - Artificial Intelligence 84 (1-2):357.
Theories for mutagenicity: a study in first-order and feature-based induction.Ashwin Srinivasan, S. H. Muggleton, M. J. E. Sternberg & R. D. King - 1996 - Artificial Intelligence 85 (1-2):277-299.
Top-down synthesis of divide-and-conquer algorithms.Douglas R. Smith - 1985 - Artificial Intelligence 27 (1):43-96.
First-order logical filtering.Afsaneh Shirazi & Eyal Amir - 2011 - Artificial Intelligence 175 (1):193-219.
Decision-theoretic planning with generalized first-order decision diagrams.Saket Joshi, Kristian Kersting & Roni Khardon - 2011 - Artificial Intelligence 175 (18):2198-2222.
Evolving interpretable decision trees for reinforcement learning.Vinícius G. Costa, Jorge Pérez-Aracil, Sancho Salcedo-Sanz & Carlos E. Pedreira - 2024 - Artificial Intelligence 327 (C):104057.
An efficient algorithm for optimal pruning of decision trees.Hussein Almuallim - 1996 - Artificial Intelligence 83 (2):347-362.
Using POMDPs for learning cost sensitive decision trees.Shlomi Maliah & Guy Shani - 2021 - Artificial Intelligence 292 (C):103400.
Universal Algorithmic Intelligence: A Mathematical Top-Down Approach.Marcus Hutter - 2007 - In Ben Goertzel & Cassio Pennachin (eds.), Artificial General Intelligence. Springer Verlag. pp. 227-290.
Analytics
Added to PP
2020-12-22
Downloads
8 (#1,345,183)
6 months
5 (#710,311)
2020-12-22
Downloads
8 (#1,345,183)
6 months
5 (#710,311)
Historical graph of downloads
Author's Profile
Citations of this work
The complexity and generality of learning answer set programs.Mark Law, Alessandra Russo & Krysia Broda - 2018 - Artificial Intelligence 259:110-146.
kLog: A language for logical and relational learning with kernels.Paolo Frasconi, Fabrizio Costa, Luc De Raedt & Kurt De Grave - 2014 - Artificial Intelligence 217 (C):117-143.
Type Extension Trees for feature construction and learning in relational domains.Manfred Jaeger, Marco Lippi, Andrea Passerini & Paolo Frasconi - 2013 - Artificial Intelligence 204 (C):30-55.
References found in this work
Solving the multiple instance problem with axis-parallel rectangles.Thomas G. Dietterich, Richard H. Lathrop & Tomás Lozano-Pérez - 1997 - Artificial Intelligence 89 (1-2):31-71.
Theories for mutagenicity: a study in first-order and feature-based induction.Ashwin Srinivasan, S. H. Muggleton, M. J. E. Sternberg & R. D. King - 1996 - Artificial Intelligence 85 (1-2):277-299.
First-order jk-clausal theories are PAC-learnable.Luc De Raedt & Sašo Džeroski - 1994 - Artificial Intelligence 70 (1-2):375-392.
PhilPapers logo by Andrea Andrews and Meghan Driscoll.
This site uses cookies and Google Analytics (see our terms & conditions for details regarding the privacy implications). Use of this site is subject to terms & conditions.
All rights reserved by The PhilPapers Foundation
Server: philpapers-web-68866f4b87-grz79 uwo