Qualified predictions for proteomics pattern diagnostics with confidence machines

Zhiyuan Luo, Tony Bellotti, Alex Gammerman

Research output: Chapter in Book/Conference proceedingBook Chapterpeer-review

6 Citations (Scopus)

Abstract

In this paper, we focus on the problem of prediction with confidence and describe the recently developed transductive confidence machines (TCM). TCM allows us to make predictions within predefined confidence levels, thus providing a controlled and calibrated classification environment. We apply the TCM to the problem of proteomics pattern diagnostics. We demonstrate that the TCM performs well, yielding accurate, well-calibrated and informative predictions in both online and offline learning settings.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsZheng Rong Yang, Richard Everson, Hujun Yin
PublisherSpringer Verlag
Pages46-51
Number of pages6
ISBN (Print)3540228810, 9783540228813
DOIs
Publication statusPublished - 2004
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3177
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

Cite this