A practical test set bound for rank learning

Nathan Drenkow, Philippe Burlina, I. Jeng Wang, Daniel Dementhon, Craig Carmen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In a world of exponentially growing data and finite computing resources, rank learning methods can play a critical role in data prioritization. While a number of new rank learning algorithms have been developed, there is a relative paucity of work to generate bounds that characterize the performance of these algorithms. When such bounds have been developed, it has often proved difficult to apply them in real-world settings. In this paper, we develop a new performance bound based on a novel application of the test set bound to rank learning. This bound can be applied to any ranking algorithm. We conduct experiments using data from the Web30K set and report results that demonstrate the tightness and validity of the test set bound for this type of application. We provide a discussion of its use for model selection as well as for comparing algorithmic performance.

Original languageEnglish (US)
Title of host publication2013 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2013
DOIs
StatePublished - Dec 1 2013
Event2013 16th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2013 - Southampton, United Kingdom
Duration: Sep 22 2013Sep 25 2013

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Other

Other2013 16th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2013
CountryUnited Kingdom
CitySouthampton
Period9/22/139/25/13

Keywords

  • Test set bound
  • generalization bounds
  • rank learning

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

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