Analysis of Perceptron-Based Active Learning
We start by showing that in an active learning setting, the Perceptron algorithm needs $\Omega(\frac{1}{\epsilon^2})$ labels to learn linear separators within generalization error $\epsilon$. We then present a simple selective sampling algorithm for this problem, which combines a modification of th...
| Autors principals: | , , |
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| Idioma: | en_US |
| Publicat: |
2005
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| Matèries: | |
| Accés en línia: | http://hdl.handle.net/1721.1/30585 |