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Classifiers That Improve With Use

Google Tech TalksJanuaary 29, 2007ABSTRACTTraining on imperfectly representative data inevitably leads to classification errors. Retraining an OCR engine with post-edited data, or even with the imperfect labels assigned by the classifier, reduces both bias and variance. Although the theoretical foundations of decision-directed adaptation are meager, it has proved successful in diverse experiments. When the operational data can be partitioned into isogenous subsets, style-constrained classification is appropriate. Patterns should be recognized in groups rather than in isolation. Shape and language context are complementary. Operator interaction should be rationalized. Only dynamic classifiers...
Length: 48:05


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