A Survey of Semi-Supervised Learning Methods

In traditional machine learning approaches to classification, one uses only a labelled set to train the classifier. Labelled instances however are often difficult, expensive, or time consuming to obtain, as they require the efforts of experienced human annotators. Meanwhile unlabeled data may be rel...

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Bibliographische Detailangaben
Hauptverfasser:	Pise, N.N., Kulkarni, P.
Format:	Tagungsbericht
Sprache:	eng ; jpn
Schlagworte:	Classification algorithms classifier Computational intelligence data expectation maximization Humans labelled learning methods Security Semi-Supervised Semisupervised learning Strontium Support vector machine classification Support vector machines test Testing training Training data unlabelled
Online-Zugang:	Volltext bestellen
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