Semi-supervised robust alternating AdaBoost

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

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva


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.

Idioma originalInglés
Título de la publicación alojadaProgress in Pattern Recognition, Image Analysis, Computer Vision and Applications - 14th Iberoamerican Conference on Pattern Recognition, CIARP 2009, Proceedings
Número de páginas8
EstadoPublicada - 2009
Evento14th Iberoamerican Conference on Pattern Recognition, CIARP 2009 - Guadalajara, Jalisco, México
Duración: 15 nov. 200918 nov. 2009

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen5856 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349


Conferencia14th Iberoamerican Conference on Pattern Recognition, CIARP 2009
CiudadGuadalajara, Jalisco


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