Robust Boosting for Learning from Few Examples

Abstract

The authors present and analyze a novel regularization technique based on enhancing their data set with corrupted copies of their original data. The motivation is that since the learning algorithm lacks information about which parts of the data are reliable, it has to make more robust classification functions. Using this framework, they propose a simple addition to the gentle boosting algorithm that enables it to work with only a few examples. They test this new algorithm on a variety of data sets and show convincing results.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2006
Accession Number
ADA455241

Entities

People

  • Ian Martin
  • Lior Wolf

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Artificial Intelligence
  • Classification
  • Cognitive Science
  • Computer Vision
  • Contracts
  • Detection
  • Gaussian Noise
  • Learning
  • Low Resolution
  • Machine Learning
  • Noise
  • Object Recognition
  • Recognition
  • Supervised Machine Learning
  • Training

Fields of Study

  • Computer science

Readers

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