VadaBoost iteratively minimizes a cost function that balances the sample mean and the sample variance of the exponential loss. Each step of the proposed algorithm minimizes the cost efficiently by providing weighted data to a weak learner rather than requiring a brute force evaluation of all possible weak learners.
This paper proposes a novel boosting algorithm called VadaBoost which is mo- tivated by recent empirical Bernstein bounds. VadaBoost iteratively minimizes a.
This paper proposes a novel boosting algorithm called VadaBoost which is mo- tivated by recent empirical Bernstein bounds. VadaBoost iteratively minimizes a.
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Does AdaBoost reduce variance?
What are the cons of AdaBoost?
This is a recent ensemble method called Variance Penalizing AdaBoost. The only difference to AdaBoost is that the weighting function tries to minimize both ...
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Experiments on a large number of datasets show significant performance gains over AdaBoost. This paper shows that sample variance penalization could be a viable ...
Missing: Penalizing | Show results with:Penalizing
Nov 15, 2021 · It is said that bagging reduces variance and boosting reduces bias. Indeed, as opposed to the base learners both ensembling methods employ.
Missing: Penalizing | Show results with:Penalizing
Aug 15, 2020 · Variance is the variability of model prediction for a given data ... This is done to penalize the errors more than the correct predictions.
The aim of the rest of this paper is to derive an efficient, AdaBoost style algorithm for sample variance penalization. 3 LOSS FUNCTIONS. In this section, we ...
Missing: Penalizing | Show results with:Penalizing
By incorporating factors such as average margin and variance, we present a generalization error bound that is heavily related to the whole margin distribution.
... A sample variance penalization algorithm known as EBBoost was previously explored [10] . While this algorithm was simple to implement and showed significant ...