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

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...

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Bibliographic Details
Published in:Pattern recognition 2009-12, Vol.42 (12), p.3231-3240
Main Authors: Lim, Shiau Hong, Wang, Li-Lun, DeJong, Gerald
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Classification
Computer science
control theory
systems
Exact sciences and technology
Handwritten Chinese characters
Information, signal and communications theory
Model construction
Pattern recognition
Pattern recognition. Digital image processing. Computational geometry
Signal processing
Telecommunications and information theory
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Summary: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.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2008.12.012