Semi-supervised learning with the help of Parzen windows
type: Item
instance of: scholarly article
title: Semi-supervised learning with the help of Parzen windows
zbMATH Open document ID: 1237.68107
author: Shao-Gao Lv
author: Yun-Long Feng
published in: Journal of Mathematical Analysis and Applications
publication date: 2011-10-21T00:00:00Z
review text: Learning theory provides mathematical foundations of many algorithms for learning from examples. A large class of learning algorithms are generated by kernel-based regularization schemes. There are two kinds of kernel-based regularization schemes: one for supervised learning and the other for semi-supervised learning or unsupervised learning. In this paper the authors introduce Parzen windows to the classical least squares regularized regression with the motivation of using unlabelled data for semi-supervised learning. The main results are an error analysis and satisfactory learning rates for the learning algorithm.
reviewed by: Ding-Xuan Zhou
Mathematics Subject Classification ID: 68Q32
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 5960923
zbMATH Keywords: semi-supervised learning
zbMATH Keywords: graph-based models
zbMATH Keywords: support vector machine
zbMATH Keywords: least square regression, reproducing kernel Hilbert spaces
describes a project that uses: KernSmooth
MaRDI profile type: Publication
full work available at URL: j.jmaa.2011.07.059
OpenAlex ID: W2043062885
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DOI: 10.1016/J.JMAA.2011.07.059
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description: scientific article; zbMATH DE number 5960923