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Abstract ||
Fundamental Limits of Passive and Active Learning UIUC
Statistical learning theory is concerned with making accurate predictions on the basis of past observations. One of the main characteristics of any learning problem is its sample complexity: the minimum number of observations needed to ensure a given prediction accuracy at a given confidence level. For the most part, the focus has been on passive learning, in which the learning agent receives independent training samples. However, recently there has been increasing interest in active learning, in which past observations are used to control the process of gathering future observations. The main question is whether active learning is strictly more powerful than its passive counterpart. One way to answer this is to compare the sample complexities of passive and active learning for the same accuracy and confidence.
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