Semi-supervised robust alternating AdaBoost

Héctor Allende-Cid, Jorge Mendoza, Héctor Allende, Enrique Canessa

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Semi-Supervised Learning is one of the most popular and emerging issues in Machine Learning. Since it is very costly to label large amounts of data, it is useful to use data sets without labels. For doing that, normally we uses Semi-Supervised Learning to improve the performance or efficiency of the classification algorithms. This paper intends to use the techniques of Semi-Supervised Learning to boost the performance of the Robust Alternating AdaBoost algorithm. We introduce the algorithm RADA+ and compare it with RADA, reporting the performance results using synthetic and real data sets, the latter obtained from a benchmark site.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis, Computer Vision and Applications - 14th Iberoamerican Conference on Pattern Recognition, CIARP 2009, Proceedings
Number of pages8
StatePublished - 2009
Event14th Iberoamerican Conference on Pattern Recognition, CIARP 2009 - Guadalajara, Jalisco, Mexico
Duration: 15 Nov 200918 Nov 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5856 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th Iberoamerican Conference on Pattern Recognition, CIARP 2009
CityGuadalajara, Jalisco


  • Expectation maximization
  • Machine ensembles
  • Robust alternating AdaBoost
  • Semi-Supervised Learning


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