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This version of boosting procedure outperforms other boosting variants for computer vision tasks (e.g., face detection) [21].

ATheach run of LOOCV, the training imaximumave beenumbertered, and then the twofstage boostiterationsch described in Section 3.2 has been performed on each clusedr.

Finally, before a new iteration the boosting procedure makes the following multiplicative update to the weights corresponding to each training sample: (15).

The best weak classifier is hence selected in each iteration of the boosting procedure such that the cost of (11) is the lowest as follows: (14).

We use a boosting procedure on patches represented as gray codes to build a strong classifier useful to distinguish between eye and non-eye patches.

The use of a priori information about the human face along with determining the significance of the image features based on the boosting procedure allows reducing the number of dimensions of the LBP feature space to approximately 250 (with the significance threshold (delta =0.95)).

From the design standpoint, we are interested in studying the following issues: (a) effectiveness of boosting when applied in this modeling framework along with its numeric quantification, and (b) impact of information granularity at which the granular models are developed on the improvements offered by boosting procedure itself.

The main idea is to apply a boosting procedure to each of the features, i.e. to force the Adaboost algorithm to make decisions on assigning the image to one class or another separately by each singular feature (x_i^j), (i=1.n; j=1.1218).

Using the boosting procedure, the therapeutic window was largely enhanced.

In other words, ν controls the learning rate of the boosting procedure.

Specifically, we use an alternating decision tree, which improves the performance of decision trees using the boosting procedure [ 36].

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