Robert E. Schapire  Home/Search
Context Related
Links: ACM DBLP | anu.edu.au/~raetsch/ps/Sch90.ps.gz 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: anu.edu.au/~raetsch/ps/ (more) (Enter author homepages) |
Rate this article:      (best)Comment on this article |
Abstract: This paper addresses the problem of improving the accuracy of an hypothesis output by a learning algorithm in the distribution-free (PAC) learning modol, A coucept class is learntble (or strongly learnable) if, given access to a source of examples of the unknown concept, the learner with high probability is able to output an hypothesis that is correct on all but aa arbitrarily small h'action of the instances. The concept class is weakly learmtble if tbe learner can preduce an hypothesis that... (Update)
Cited by: More
Comparing Knowledge-Based Sampling to - Boosting Martin Scholz (Correct)
The Margin Vector, Admissible Loss and Multi-class.. - Zou, Zhu, Hastie (Correct)
Sampling-Based Sequential Subgroup Mining - Martin Scholz Artificial (Correct)
Similar documents based on text: More All
0.3: General Bounds on Statistical Query Learning and PAC Learning .. - Aslam, Decatur (1993) (Correct)
0.2: Boosting a Weak Learning Algorithm By Majority - Freund (1995) (Correct)
0.2: On the Sample Complexity of Weakly Learning - Goldman, Kearns, al. (1992) (Correct)
Related documents from co-citation: More All
44: Boosting a weak learning algorithm by majority - Freund - 1995
28: A decision-theoretic generalization of on-line learning and an application to bo.. - Freund, Schapire - 1997
27: Programs for machine learning (context) - Quinlan - 1993
BibTeX entry: (Update)
Robert E. Schapire. The strength of weak learnability. Machine Learning, 5(2):197--227, 1990. http://citeseer.ist.psu.edu/schapire90strength.html More
@article{ schapire90strength,
author = "Robert E. Schapire",
title = "The Strength of Weak Learnability",
journal = "Machine Learning",
volume = "5",
pages = "197-227",
year = "1990",
url = "citeseer.ist.psu.edu/schapire90strength.html" }
Citations not processed or no citations identified.
The graph only includes citing articles where the year of publication is known.
Documents on the same site (http://mlg.anu.edu.au/~raetsch/ps/): More
On the Convergence of Leveraging - Rätsch, Mika, Warmuth (2002) (Correct)
Meta Learning: Learning to Predict the Leave-one-out Error - Tsuda, Rätsch, Mika, Müller (Correct)
Constructing Boosting Algorithms from SVMs: an.. - Rätsch, Mika.. (2002) (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