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Learning to Combine Discriminative Classifiers

Learning to Combine Discriminative Classifiers

This video was recorded at 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Washington 2010. Much of research in data mining and machine learning has led to numerous practical applications. Spam filtering, fraud detection, and user query-intent analysis has relied heavily on machine learned classifiers, and resulted in improvements in robust classification accuracy. Combining multiple classifiers (a.k.a. Ensemble Learning) is a well studied and has been known to improve effectiveness of a classifier. To address two key challenges in Ensemble Learning-- (1) learning weights of individual classifiers and (2) the combination rule of their weighted responses, this paper proposes a novel Ensemble classifier, EnLR, that computes weights of responses from... Show More

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