COMS 6998 - 4 Fall 2017 Presenter : Geelon So
نویسنده
چکیده
In the setting of active learning, the data comes unlabeled and querying the label of a data point is expensive. The goal of an active learner is to reduce the number of labels needed and output a hypothesis with error rate ≤ . Recall that the usual sample complexity of supervised learning is Ω(1/ ). The motivation for defining splitting index is to characterize the sample complexity of active learning. In the following parts of this section, we give some examples to show that the label complexity depends on the underlying distribution P and the target hypothesis h∗.
منابع مشابه
COMS 6998 - 4 Fall 2017 Presenter : Geelon So
Recall that in the active learning setting, learner is provided with unlabeled samples, and can query teacher for the label. The goal is to learn a concept close enough to the target concept while minimizing the number of labels queried. Ideally the number of labels needed is much smaller than Ω(1/ ), which is the number of labeled examples in the passive learning setting. To characterize the s...
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+ errPn(hn)− errPn(h) + errPn(h ∗)− errP (h∗). The second part is less than or equals to 0. We can disregard it when we aim at deriving an upper bound of the regret. Since the target function h∗ is independent of the sample pairs, the third part can be bounded easily by analyzing the binomial distribution with success probability errP (h∗) and n trials. To analyze the remaining first part, we b...
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