Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-10268-4_68 | CIARP |
Keywords | Field | DocType |
performance result,algorithm rada,semi-supervised learning,robust alternating adaboost algorithm,machine learning,classification algorithm,large amount,semi-supervised robust alternating adaboost,benchmark site,semi supervised learning,expectation maximization | Online machine learning,AdaBoost,Semi-supervised learning,Stability (learning theory),Pattern recognition,Computer science,Unsupervised learning,Boosting (machine learning),Artificial intelligence,Ensemble learning,BrownBoost,Machine learning | Conference |
Volume | ISSN | Citations |
5856 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Héctor Allende-cid | 1 | 22 | 12.60 |
| Jorge Mendoza | 2 | 28 | 2.05 |
| Héctor Allende | 3 | 148 | 31.69 |
| Enrique Canessa | 4 | 33 | 9.82 |