Semi-supervised robust alternating AdaBoost
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN
1611-3349
Date Issued
2009-12-01
Author(s)
Mendoza, Jorge
Canessa, Enrique
DOI
10.1007/978-3-642-10268-4_68
Abstract
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, re-
porting the performance results using synthetic and real data sets, the
latter obtained from a benchmark site.
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, re-
porting the performance results using synthetic and real data sets, the
latter obtained from a benchmark site.
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