Article thumbnail

Statistical Learning From Non-Metric Data Anonymous Author(s) Affiliation

By 

Abstract

Address email Non-metric pairwise proximity data are frequently encountered in practical applications. Although practitioners use various ways of adjustments and corrections, a complete and well-sounded statistical learning theory applicable to such data is still missing. Machine learning would greatly benefit from methods that could generalize from settings close to human perception, or otherwise non-metric. In this paper, we provide both theoretical and experimental evidence about the existence of such methods. This enables the foundation needed to define statistical learning from non-metric data

Topics: Similarity and Distance Learning, Learning with Structured Data
Year: 2008
OAI identifier: oai:CiteSeerX.psu:10.1.1.122.6667
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://ict.ewi.tudelft.nl/~art... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.