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Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing.
This solution takes advantages of a set of carefully designed features and well-tuned boosting algorithms.
For non-parametric estimation problems, functional gradient descent type (also called boosting) algorithms are used to estimate the best fit in infinite dimensional function spaces.
It may be noted that incorporation of the bagging and stochastic gradient boosting algorithms in DTF and DTB models, respectively resulted in their enhanced predictive ability.
We discuss limitations of shallow architectures, such as so-called kernel machines, boosting algorithms, and one-hidden-layer artificial neural networks.
Eye movement directions in response to a visual stimulus for tracking an object are classified using ensemble classifiers based on bagging and adaptive boosting algorithms.
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Boosting-based approach is considered as any image-based approach that uses the boosting algorithm in the classification stage.
We study SBA, a hierarchical boosting algorithm proposed by Schapire, in terms of its representation and its search.
The boosting algorithm calls this 'weak' learner repeatedly, at each stage feeding it a different subset of the training examples.
This technique implements the stochastic gradient boosting algorithm with a tree-based method.
This paper presents a novel boosting algorithm for genetic learning of fuzzy classification rules.
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