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Totally, 174 patients were included in this analysis and named as "learning sample".
Boosting is a method of combining ensemble classifiers created from a weighted version of learning sample, where weights have been adjusted at each step to provide increased weight to cases misclassified earlier.
The small sample effects include: (i) training bias, i.e. learning sample size influence on generalization error of the base experts or of the fusion rule, (ii) optimistic biased outputs of the experts (self-boasting effect) and (iii) sample size impact on determining optimal complexity of the fusion rule.
The computational complexity of the Random Forests and Extra-Trees algorithms is on the order of
A learning sample L n based on a random selection of n i.i.d.i.d
Additionally, a learning sample of 5 patients will be incorporated to allow the surgeons to familiarize themselves with the novel prototype software.
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In succession, the segmentation areas are modeled by a structural SVM as learning samples to achieve more effective online tracking.
We then obtained the corresponding discriminant functions through training a set of data from engineering examples as learning samples and evaluated their criteria by a back substitution method to verify the optimal properties of the model.
In the future, we are going to acquire sequential online degradation measurements for real-time performance degradation assessment and detection, and enlarge the number of fault and learning samples for more accurate fault diagnosis.
Sugano et al. [1] take the cropped eye region as a point in a local manifold model and make gaze estimation by clustering learning samples with similar head poses and constructing their local manifold model.
Next, to avoid selection bias from a pattern of selection of learning samples, we repeat the entire process 20 times by shuffling samples at every 10-fold CV.
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