Semi-supervised robust alternating AdaBoost

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

doi:10.1007/978-3-642-10268-4_68

Semi-supervised robust alternating AdaBoost

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

Faculty of Engineering and Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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, reporting the performance results using synthetic and real data sets, the latter obtained from a benchmark site.

Original language	English
Title of host publication	Progress in Pattern Recognition, Image Analysis, Computer Vision and Applications - 14th Iberoamerican Conference on Pattern Recognition, CIARP 2009, Proceedings
Pages	579-586
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-10268-4_68
State	Published - 2009
Event	14th Iberoamerican Conference on Pattern Recognition, CIARP 2009 - Guadalajara, Jalisco, Mexico Duration: 15 Nov 2009 → 18 Nov 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5856 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th Iberoamerican Conference on Pattern Recognition, CIARP 2009
Country/Territory	Mexico
City	Guadalajara, Jalisco
Period	15/11/09 → 18/11/09

Keywords

Expectation maximization
Machine ensembles
Robust alternating AdaBoost
Semi-Supervised Learning

Access to Document

10.1007/978-3-642-10268-4_68

Cite this

Allende-Cid, H., Mendoza, J., Allende, H., & Canessa, E. (2009). Semi-supervised robust alternating AdaBoost. In Progress in Pattern Recognition, Image Analysis, Computer Vision and Applications - 14th Iberoamerican Conference on Pattern Recognition, CIARP 2009, Proceedings (pp. 579-586). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5856 LNCS). https://doi.org/10.1007/978-3-642-10268-4_68

Allende-Cid, Héctor ; Mendoza, Jorge ; Allende, Héctor et al. / Semi-supervised robust alternating AdaBoost. Progress in Pattern Recognition, Image Analysis, Computer Vision and Applications - 14th Iberoamerican Conference on Pattern Recognition, CIARP 2009, Proceedings. 2009. pp. 579-586 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Semi-supervised robust alternating AdaBoost",

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, reporting the performance results using synthetic and real data sets, the latter obtained from a benchmark site.",

keywords = "Expectation maximization, Machine ensembles, Robust alternating AdaBoost, Semi-Supervised Learning",

author = "H{\'e}ctor Allende-Cid and Jorge Mendoza and H{\'e}ctor Allende and Enrique Canessa",

note = "Funding Information: This work was supported in part by Research Grant Fondecyt 1070220, and DGIP-UTFSM Grant.; 14th Iberoamerican Conference on Pattern Recognition, CIARP 2009 ; Conference date: 15-11-2009 Through 18-11-2009",

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doi = "10.1007/978-3-642-10268-4_68",

language = "English",

isbn = "3642102670",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Allende-Cid, H, Mendoza, J, Allende, H & Canessa, E 2009, Semi-supervised robust alternating AdaBoost. in Progress in Pattern Recognition, Image Analysis, Computer Vision and Applications - 14th Iberoamerican Conference on Pattern Recognition, CIARP 2009, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5856 LNCS, pp. 579-586, 14th Iberoamerican Conference on Pattern Recognition, CIARP 2009, Guadalajara, Jalisco, Mexico, 15/11/09. https://doi.org/10.1007/978-3-642-10268-4_68

Semi-supervised robust alternating AdaBoost. / Allende-Cid, Héctor; Mendoza, Jorge; Allende, Héctor et al.
Progress in Pattern Recognition, Image Analysis, Computer Vision and Applications - 14th Iberoamerican Conference on Pattern Recognition, CIARP 2009, Proceedings. 2009. p. 579-586 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5856 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Allende-Cid H, Mendoza J, Allende H, Canessa E. Semi-supervised robust alternating AdaBoost. In Progress in Pattern Recognition, Image Analysis, Computer Vision and Applications - 14th Iberoamerican Conference on Pattern Recognition, CIARP 2009, Proceedings. 2009. p. 579-586. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-10268-4_68

Semi-supervised robust alternating AdaBoost

Abstract

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Keywords

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