Classifiers That Improve With Use

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Published on October 9, 2007 3:48:16 AM ● Video Link: https://www.youtube.com/watch?v=-Rj03JDrhrM

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Google Tech Talks
Januaary 29, 2007

ABSTRACT

Training 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...