Bibliographic Details
| Title: |
Integrating prior domain knowledge into discriminative learning using automatic model construction and phantom examples |
| Authors: |
Lim, Shiau Hong shonglim@illinois.edu, Wang, Li-Lun1, DeJong, Gerald1 |
| Source: |
Pattern Recognition. Dec2009, Vol. 42 Issue 12, p3231-3240. 10p. |
| Subjects: |
Machine learning, Chinese character sets (Data processing), Pattern perception, Robust control, Computer science |
| Abstract: |
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. [Copyright &y& Elsevier] |
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Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |
