English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  Semi-Supervised Induction

Yu, K., Tresp, V., & Zhou, D.(2004). Semi-Supervised Induction (141).

Item is

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Yu, K, Author
Tresp, V, Author
Zhou, D1, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

Content

show
hide
Free keywords: -
 Abstract: Considerable progress was recently achieved on semi-supervised learning, which differs from the traditional supervised learning by additionally exploring the information of the unlabelled examples. However, a disadvantage of many existing methods is that it does not generalize to unseen inputs. This paper investigates learning methods that effectively make use of both labelled and unlabelled data to build predictive functions, which are defined on not just the seen inputs but the whole space. As a nice property, the proposed method allows effcient training and can easily handle new test points. We validate the method based on both toy data and real world data sets.

Details

show
hide
Language(s):
 Dates: 2004-08
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: Report Nr.: 141
BibTex Citekey: 2782
 Degree: -

Event

show

Legal Case

show

Project information

show

Source

show