MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • Computer Science and Artificial Intelligence Lab (CSAIL)
  • CSAIL Digital Archive
  • CSAIL Technical Reports (July 1, 2003 - present)
  • View Item
  • DSpace@MIT Home
  • Computer Science and Artificial Intelligence Lab (CSAIL)
  • CSAIL Digital Archive
  • CSAIL Technical Reports (July 1, 2003 - present)
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Cascading Regularized Classifiers

Author(s)
Perez-Breva, Luis
Thumbnail
DownloadMIT-CSAIL-TR-2004-023.ps (8640.Kb)
Additional downloads
Metadata
Show full item record
Abstract
Among the various methods to combine classifiers, Boosting was originally thought as an stratagem to cascade pairs of classifiers through their disagreement. I recover the same idea from the work of Niyogi et al. to show how to loosen the requirement of weak learnability, central to Boosting, and introduce a new cascading stratagem. The paper concludes with an empirical study of an implementation of the cascade that, under assumptions that mirror the conditions imposed by Viola and Jones in [VJ01], has the property to preserve the generalization ability of boosting.
Date issued
2004-04-21
URI
http://hdl.handle.net/1721.1/30463
Other identifiers
MIT-CSAIL-TR-2004-023
AIM-2004-028
Series/Report no.
Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
Keywords
AI

Collections
  • CSAIL Technical Reports (July 1, 2003 - present)

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.