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Sequential training could be investigated and combined with a boosting framework.
An ensemble of shallower decision trees, if learned in a boosting framework, can guard against overfitting because of the iterative nature of the learning and the additive structure of the model [ 31].
Mallapagada et al., [ 38] presented a boosting framework for semi-supervised learning to improve supervised learning, termed as SemiBoost, by using both labeled data and unlabeled data in the learning process.
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We propose a deep boosting framework based on layer-by-layer joint feature boosting and dictionary learning.
In this paper, we present a generalized boosting framework to tackle some challenging problems in practical remote iris recognition, namely, iris detection, iris mislocalization detection, iris spoof detection as well as iris recognition.
As a third method, we tested the C-index boosting algorithm, which has been presented as an alternative means for the derivation of marker (gene) combinations via a gradient boosting framework and the direct optimization of the C-index [ 24].
Our method is based on a semi-supervised boosting framework that samples some useful unlabeled data at each iteration to improve the performance of the underlying classifier (SVM in this paper).
These methods train a discriminative classifier under the boosting framework.
The tracking task is formulated as a binary classification via online boosting framework.
For more details on including backward removal or even a floating searching capability into the boosting framework, please refer to [24].
In this study, we propose a new method that combines the semi-supervised boosting framework with the underlying supervised learning algorithm SVM to predict hot spots.
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