• Documents
  • Authors
  • Tables

CiteSeerX logo

Advanced Search Include Citations
Advanced Search Include Citations

DMCA

MiniBoosting Decision Trees (1999)

Cached

  • Download as a PDF

Download Links

  • [www.prip.tuwien.ac.at]
  • [hunch.net]
  • [web.cs.sunyit.edu]
  • [web.cs.sunyit.edu]
  • [web.cs.sunyit.edu]
  • [hunch.net]
  • [www.hunch.net]
  • [publications.ai.mit.edu]
  • [publications.ai.mit.edu]
  • [www.cs.sunyit.edu]
  • [www.dmi.unict.it]
  • [www.cse.unsw.edu.au]
  • [www.cse.unsw.edu.au]
  • [www.cs.toronto.edu]
  • [storm.cis.fordham.edu]
  • [www.di.unipi.it]
  • [impact.asu.edu]
  • [impact.asu.edu]
  • [courses.cs.ut.ee]
  • [www.cs.utah.edu]
  • [www.cs.utah.edu]
  • [storm.cis.fordham.edu]
  • [sci2s.ugr.es]
  • [storm.cis.fordham.edu]
  • [impact.asu.edu]
  • [impact.asu.edu]
  • [courses.cs.ut.ee]

  • Other Repositories/Bibliography

  • DBLP
  • Save to List
  • Add to Collection
  • Correct Errors
  • Monitor Changes
by J. R. Quinlan
Citations:4361 - 4 self
  • Summary
  • Citations
  • Active Bibliography
  • Co-citation
  • Clustered Documents
  • Version History

BibTeX

@MISC{Quinlan99miniboostingdecision,
    author = {J. R. Quinlan},
    title = {MiniBoosting Decision Trees},
    year = {1999}
}

Share

Facebook Twitter Reddit Bibsonomy

OpenURL

 

Abstract

Boosting, introduced by Freund and Schapire, is a method for generating an ensemble of classifiers by successive reweightings of the training cases. We study boosting in the context of small ensembles of decision trees, showing that the reweighting procedure can be improved and that the resulting ensemble can be represented by a single decision tree. This tree is large, attesting to the complexity of boosted classifiers, but simplifications that do not affect its performance on the training data destroy the benefits of boosting. 1. Introduction Learning to predict the categories to which objects belong by analysing a collection of training cases is one of the most studied areas of Machine Learning. Well-tried methods for constructing a classifier or function that maps from object descriptions to class names include instance-based approaches (Aha, Kibler, & Albert, 1991), neural networks (McClelland & Rumelhart, 1988), decision trees (Breiman, Friedman, Olshen, & Stone, 1984; Quinlan, ...

Keyphrases

decision tree    training case    neural network    boosted classifier    training data    studied area    successive reweightings    instance-based approach    machine learning    small ensemble    object description    single decision tree    reweighting procedure    mcclelland rumelhart    class name    well-tried method   

Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University