1. Home
  2. Material Detail: Transductive Rademacher complexities for learning over a graph

Material Detail

Transductive Rademacher complexities for learning over a graph

Transductive Rademacher complexities for learning over a graph

This video was recorded at 6th Slovenian International Conference on Graph Theory, Bled 2007. Recent investigations indicate the use of a probabilistic "learning" perspective of tasks defined on a single graph, as opposed to the traditional algorithmical "computational" point of view. This note discusses the use of Rademacher complexities in this setting, and illustrates the use of Kruskal's algorithm for transductive inference based on a nearest neighbor rule.
Keywords:
videolectures, ocwc, oec
Disciplines:
Go to Material 
Bookmark / Add to Course ePortfolio
Create a Learning Exercise
Add Accessibility Information
Rate
Add a Comment

Quality

  • User Rating
  • Comments
  • Learning Exercises
  • Bookmark Collections
  • Course ePortfolios
  • Accessibility Info

More about this material

Material Type:
Presentation
Date Added to MERLOT:
February 10, 2015
Date Modified in MERLOT:
February 10, 2015
Author:
Submitter:
The Open Education Consortium
Primary Audience:
College General Ed, College Lower Division, College Upper Division
Technical Format:
Video
Mobile Compatibility:
Not specified at this time
Language:
English
Cost Involved:
No
Source Code Available:
No
Accessibility Information Available:
Unknown
Creative Commons:
Creative Commons License
This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States

Browse...

Disciplines with similar materials as Transductive Rademacher complexities for learning over a graph
Other materials like Transductive Rademacher complexities for learning over a graph

Comments

Log in to participate in the discussions or sign up if you are not already a MERLOT member.