Statistical machine learning has achieved great success in many fields in the last few decades.However, there remain classification problems that computers still struggle to match human performance.Many such problems share the same properties---large within class variability and complex structure in the examples, which is often true for real world objects.This does not mean lack of information for classification in the examples.On the contrary, there is still a clear pattern in the examples, but hidden behind a many-way covariance structure such that useful information is too dilute for conventional statistical machine learners to pick up.However, if we can exploit the structural nature of the objects and concentrate information about the classification, the problem can become much easier.In this dissertation we propose a framework using prior knowledge about modeling the structures in the examples to concentrate information for classification.The framework is instantiated to the task of classifying pairs of similar offline handwritten Chinese characters.We empirically demonstrate that our proposed framework indeed concentrates useful information for classification and makes the classification problem easier for statistical learning.Our approach advances the state of the art both in offline handwritten character recognition and in machine learning.
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Use of prior knowledge in classification of similar and structured objects
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