Integrating prior domain knowledge into discriminative learning using automatic model construction and phantom examples

Shiau Hong Lim, Li Lun Wang, Gerald DeJong

Research output: Contribution to journalArticle

Abstract

Domain knowledge captures an expert's approximate understanding of the world, its objects, and their properties. When available, it should serve to augment the information in a classification learner's training set. But this form of prior knowledge does not easily fit into the statistical learning paradigm. We propose and evaluate the use of phantom examples to remedy this. Our system performs automated model construction and learns generative models for phantom examples that adapt to the need of individual tasks. The approach is validated on the challenging real-world task of distinguishing handwritten Chinese characters. The approach improves learning significantly, provides additional robustness, and works well even though the domain knowledge is imperfect and approximate.

Original languageEnglish (US)
Pages (from-to)3231-3240
Number of pages10
JournalPattern Recognition
Volume42
Issue number12
DOIs
StatePublished - Dec 1 2009

Keywords

  • Classification
  • Handwritten Chinese characters
  • Model construction

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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