To obtain the output coming from each weak learner, o t ( x ) , it will be desirable maximize the gradient of training error bound (3), so that a maximum ...
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Jan 20, 2006 · After proving that the traditional RA emphasis function can be seen as the product of two factors, the first depending on the quadratic error of ...
Request PDF | Boosting by weighting critical and erroneous samples | Real Adaboost is a well-known and good performance boosting method used to build ...
(PDF) Boosting by weighting critical and erroneous samples ...
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2005. Abstract. This paper shows that new and exible criteria to resample populations in boosting algorithms can lead to performance improvements.
Boosting by weighting boundary and erroneous samples. ∗. Vanessa Gómez-Verdejo, Manuel Ortega-Moral,. Jerónimo Arenas-Garcıa and Anıbal R. Figueiras-Vidal.
Missing: critical | Show results with:critical
This paper shows that new and flexible criteria to resample populations in boosting algorithms can lead to performance improvements.
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First, if ht has a small weighted error εt, then αt is large, so that ht will have a huge voting power in the final vote, which is a desirable property. Second, ...
For some problems, certain selections of λ resulted in a much faster initial convergence although the final error is higher than the results displayed in Table ...
Missing: critical | Show results with:critical
In boosting, the weights are an immediate function of the learned error. Note that while boosting is a very good algorithm in the general case, it is difficult.