Graph-based semi-supervised learning /

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While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of...

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Hlavní autoři: Subramanya, Amarnag (Autor), Talukdar, Partha Pratim (Autor)
Médium: E-kniha
Jazyk:English
Vydáno: San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, 2014.
Edice:Synthesis digital library of engineering and computer science.
Synthesis lectures on artificial intelligence and machine learning ; # 29.
Témata:
Machine learning > Graphic methods.
Supervised learning (Machine learning)
graph Laplacian
graph-based learning
graph-based semi-supervised learning
inductive learning
label propagation
manifold learning
nonparametric learning
scalable machine learning
semi-supervised learning
transductive learning
On-line přístup:Abstract with links to full text

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Abstract with links to full text

MG University

Informace o exemplářích z: MG University

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