Michael Kearns  Home/Search
Context Related
| att.com/~mkearns/papers/topdown.ps.Z upenn.edu/~mkearns/paper...topdown.ps.Z Cached: PS.gz PS PDF This document uses CoBlitz to cache paper downloads. If your firewall is blocking outgoing connections to port 3125, you can use these links to download local copies.
Image Update HelpPS.gz PS PDF From: cs.ualberta.ca/~greiner...project (more) (Enter author homepages) |
Rate this article:      (best)Comment on this article |
Abstract: We analyze the performance of top-down algorithms for decision tree learning, such as those employed by the widely used C4.5 and CART software packages. Our main result is a proof that such algorithms are boosting algorithms. By this we mean that if the functions that label the internal nodes of the decision tree can weakly approximate the unknown target function, then the top-down algorithms we study will amplify this weak advantage to build a tree achieving any desired level of accuracy. The... (Update)
Cited by: More
Learning Decision Trees Using the Area Under the ROC Curve - Csar Ferri Cferri (2002) (Correct)
Constituent Parsing by Classification - Turian, Melamed (2005) (Correct)
Learning Multi-label Alternating Decision Trees from.. - De Comite, Gilleron.. (Correct)
Similar documents (at the sentence level):
65.0%: On the Boosting Ability of Top-Down Decision Tree Learning.. - Kearns, Mansour (1996) (Correct)
Active bibliography (related documents): More All
0.3: Applying the Weak Learning Framework to Understand and.. - Dietterich, Kearns.. (1996) (Correct)
0.1: Learning Functions Represented as Multiplicity Automata - Beimel, Bergadano.. (2000) (Correct)
0.1: Decision Trees: More Theoretical Justification for Practical.. - Pechyony (2004) (Correct)
Similar documents based on text: More All
0.3: Boosting Theory Towards Practice: - Recent Developments In (Correct)
0.3: An Experimental and Theoretical Comparison of Model.. - Kearns, Mansour, Ng, Ron (Correct)
0.3: A Sparse Sampling Algorithm for Near-Optimal Planning in.. - Kearns, Mansour, Ng (2001) (Correct)
Related documents from co-citation: More All
15: Programs for machine learning (context) - Quinlan - 1993
14: A decision-theoretic generalization of on-line learning and an application to bo.. - Freund, Schapire - 1997
11: Experiments with a New Boosting Algorithm - Freund, Schapire - 1996
BibTeX entry: (Update)
Kearns, M., & Mansour, Y. (1996). On the boosting ability of top-down decision tree learning algorithms. In Proceedings of the Twenty-Eighth Annual ACM Symposium on the Theory of Computing. http://citeseer.ist.psu.edu/article/kearns96boosting.html More
@inproceedings{ kearns96boosting,
author = "Michael Kearns and Yishay Mansour",
title = "On the boosting ability of top-down decision tree learning algorithms",
pages = "459--468",
year = "1996",
url = "citeseer.ist.psu.edu/article/kearns96boosting.html" }
Citations (may not include all citations):2177 Programs for Machine Learning (context) - Quinlan - 1993
509 A decision-theoretic generalization of on-line learning and .. - Freund, Schapire - 1995
493 Communications of the ACM (context) - Valiant, of et al. - 1984
273 The strength of weak learnability - Schapire - 1990
190 Wadsworth International Group (context) - Breiman, Friedman et al. - 1984
184 Cryptographic limitations on learning boolean formulae and f.. - Kearns, Valiant - 1994
180 Boosting a weak learning algorithm by majority - Freund - 1995
115 Efficient distribution-free learning of probabilistic concep.. - Kearns, Schapire - 1994
96 Learning decision trees using the Fourier spectrum - Kushilevitz, Mansour - 1991
92 An empirical comparison of selection measures for decision-t.. (context) - Mingers - 1989
74 Exact learning via the monotone theory (context) - Bshouty - 1993
70 A further comparison of splitting rules for decision-tree in.. (context) - Buntine, Niblett - 1992
63 Weakly learning DNF and characterizing statistical query lea.. - Blum, Furst et al. - 1994
46 An efficient membership query algorithm for learning DNF wit.. - Jackson - 1994
29 Applying the weak learning framework to understand and impro.. - Dietterich, Kearns et al. - 1996
10 Simple learning algorithms for decision trees and multivaria.. - Bshouty, Mansour - 1995
4 Some experiments with a new boosting algorithm (context) - Freund, Schapire - 1996
The graph only includes citing articles where the year of publication is known.
Documents on the same site (http://www.cs.ualberta.ca/~greiner/C-651/project.html): More
On-Line Portfolio Selection Using Multiplicative Updates - Helmbold, Schapire, Singer, .. (1998) (Correct)
Efficient Distribution-free Learning of Probabilistic Concepts - Kearns, Schapire (1993) (Correct)
Applying the Weak Learning Framework to Understand and.. - Dietterich, Kearns.. (1996) (Correct)
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC