Offline Recognition of Chinese Handwriting by Multifeature and Multilevel Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Efficient Visual Recognition Using the Hausdorff Distance
Efficient Visual Recognition Using the Hausdorff Distance
Training Invariant Support Vector Machines
Machine Learning
Restricted Bayes Optimal Classifiers
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Analysis and Recognition of Asian Scripts - the State of the Art
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Handbook Of Pattern Recognition And Computer Vision
Handbook Of Pattern Recognition And Computer Vision
Hybrid generative/discriminative classifier for unconstrained character recognition
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Virtual example synthesis based on PCA for off-line handwritten character recognition
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
A Bayesian network modeling approach for cross media analysis
Image Communication
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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.