On weak base Hypotheses and their implications for boosting regression and classification
نویسندگان
چکیده
منابع مشابه
On Weak Base Hypotheses and Their Implications for Boosting Regression and Classification By
When studying the training error and the prediction error for boosting, it is often assumed that the hypotheses returned by the base learner are weakly accurate, or are able to beat a random guesser by a certain amount of difference. It has been an open question how much this difference can be, whether it will eventually disappear in the boosting process or be bounded by a positive amount. This...
متن کاملOn Weak Base Hypotheses and Their Implications
1 2 When studying the training error and the prediction error for boosting, it is often assumed that the hypotheses returned by the base learner are weakly accurate, or are able to beat a random guesser by a certain amount of diierence. It is has been an open question how much this diierence can be, whether it will eventually disappear in the boosting process or be bounded by a nite amount see,...
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The most basic property of the boosting algorithm is its ability to reduce the training error, subject to the critical assumption that the base learners generate weak hypotheses that are better that random guessing. We exploit analogies between regression and classiication to give a characterization on what base learners generate weak hypotheses, by introducing a geometric concept called the an...
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In this paper we describe the first stage of a new learning system for object detection and recognition. For our system we propose Boosting [5] as the underlying learning technique. This allows the use of very diverse sets of visual features in the learning process within a common framework: Boosting — together with a weak hypotheses finder — may choose very inhomogeneous features as most relev...
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One basic property of the boosting algorithm is its ability to reduce the training error, subject to the critical assumption that the base learners generatèweak' (or more appropriately, `weakly accurate') hypotheses that are better that random guessing. We exploit analogies between regression and classiication to give a characterization on what base learners generate weak hypotheses, by introdu...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2002
ISSN: 0090-5364
DOI: 10.1214/aos/1015362184