Sunday, June 13, 2010

Active Learning

Labeling every data point is time consuming and unlabeled data is abundant. This motivates the field of active learning, in which learner is able to ask for labels of specific points, but each question is charged.

The points to be queried are usually chosen from a pool of unlabeled data points. What we need is to ask as few as possible queries and pick up points that would help the learner the most (highest information content).

Possible ideas to find the points to be queried:
1) Build a Voronoi structure and ask the points which are a) center of largest circumcirle or b) subset of Voronoi vertices whose nearest neighbors belong to different classes. It is difficult for high dimensions.
Another ideas: Use two learners and ask points where they disagree, use SVM and only ask point closest to hyperplane at each round. The question to me is how I can adapt it to effort estimation (a regression problem). We formed a reading group for this problem, the bib file etc. are here: http://unbox.org/wisp/var/ekrem/activeLearning/Literature/



20 comments:

  1. ^^~~輕輕鬆鬆的逛部落格,多謝有您的分享哦~~~........................................

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  2. 生存乃是不斷地在內心與靈魂交戰;寫作是坐著審判自己。......................................................................

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  3. 好的開始並不代表會成功,壞的開始並不代表是失敗............................................................

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  4. 這不過是滑一跤,並不是死掉而爬不起來了。..................................................

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  5. 感謝您願意分享您的生活經驗~~支持您的更新哦!..................................................................

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  6. 看到大家都留言-我也忍不住說聲---加油..................................................

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