1.0Microsoft Researchhttps://www.microsoft.com/en-us/researchLisa Clawsonhttps://www.microsoft.com/en-us/research/people/lclawson/Importance Weighted Active Learning - Microsoft Researchrich600338<blockquote class="wp-embedded-content" data-secret="Vjt6CwQzCB"><a href="https://www.microsoft.com/en-us/research/publication/importance-weighted-active-learning/">Importance Weighted Active Learning</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/importance-weighted-active-learning/embed/#?secret=Vjt6CwQzCB" width="600" height="338" title="“Importance Weighted Active Learning” — Microsoft Research" data-secret="Vjt6CwQzCB" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script type="text/javascript">
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We present a practical and statistically consistent scheme for actively learning binary classifiers under general loss functions. Our algorithm uses importance weighting to correct sampling bias, and by controlling the variance, we are able to give rigorous label complexity bounds for the learning process. Experiments on passively labeled data show that this approach reduces the […]