TAILIEUCHUNG - Báo cáo khoa học: "Active Learning with Confidence"

Active learning is a machine learning approach to achieving high-accuracy with a small amount of labels by letting the learning algorithm choose instances to be labeled. Most of previous approaches based on discriminative learning use the margin for choosing instances. We present a method for incorporating confidence into the margin by using a newly introduced online learning algorithm and show empirically that confidence improves active learning. | Active Learning with Confidence Mark Dredze and Koby Crammer Department of Computer and Information Science University of Pennsylvania Philadelphia PA 19104 mdredze crammer @ Abstract Active learning is a machine learning approach to achieving high-accuracy with a small amount of labels by letting the learning algorithm choose instances to be labeled. Most of previous approaches based on discriminative learning use the margin for choosing instances. We present a method for incorporating confidence into the margin by using a newly introduced online learning algorithm and show empirically that confidence improves active learning. 1 Introduction Successful applications of supervised machine learning to natural language rely on quality labeled training data but annotation can be costly slow and difficult. One popular solution is Active Learning which maximizes learning accuracy while minimizing labeling efforts. In active learning the learning algorithm itself selects unlabeled examples for annotation. A variety of techniques have been proposed for selecting examples that maximize system performance as compared to selecting instances randomly. Two learning methodologies dominate NLP applications probabilistic methods naive Bayes logistic regression and margin methods support vector machines and passive-aggressive. Active learning for probabilistic methods often uses uncertainty sampling label the example with the lowest probability prediction the most uncertain Lewis and Gale 1994 . The equivalent technique for margin learning associates the margin with prediction certainty label the example with the lowest margin Tong and Koller 2001 . Common intuition equates large margins with high prediction confidence. However confidence and margin are two distinct properties. For example an instance may receive a large margin based on a single feature which has been updated only a small number of times. Another example may receive a small margin but its features have

• Báo cáo khoa học
• Active Learning with Confidence
• Mark Dredze
• Koby Crammer
• báo cáo khoa học
• báo cáo ngôn ngữ
• xử lý ngôn ngữ tự nhiên
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