A noise-detection based AdaBoost algorithm for mislabeled data

Noise sensitivity is known as a key related issue of AdaBoost algorithm. Previous works exhibit that AdaBoost is prone to be overfitting in dealing with the noisy data sets due to its consistent high weights assignment on hard-to-learn instances (mislabeled instances or outliers). In this paper, a n...

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Veröffentlicht in:	Pattern recognition Jg. 45; H. 12; S. 4451 - 4465
Hauptverfasser:	Cao, Jingjing, Kwong, Sam, Wang, Ran
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 01.12.2012 Elsevier
Schlagworte:	AdaBoost Algorithms Applied sciences Dealing Ensemble learning Error analysis Exact sciences and technology Information, signal and communications theory k-NN Maximization Pattern recognition Signal and communications theory Signal processing Signal representation. Spectral analysis Signal, noise Telecommunications and information theory Training EM AdaBoost k-NN Pattern recognition Ensemble learning Nearest neighbour Automatic classification Resource allocation Iterative method Signal classification k-N Learning Outlier Loss function Resource management EM algorithm Learning algorithm
ISSN:	0031-3203, 1873-5142
Online-Zugang:	Volltext
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