Sentence examples for multiple weak classifiers from inspiring English sources

Boosting is a method of combining multiple "weak" classifiers to form a "strong" classifier.

Then, multiple weak classifiers are combined to make the final decision.

The basic idea of the algorithm is that multiple weak classifiers compose a strong individual classifier.

Ensemble learning often provides a framework to generate multiple weak classifiers and aggregates these by using an integration rule to become a strong classifier.

The mechanism of a decision tree can utilize different features to compensate each other, and the ensemble of decision trees combines multiple weak tree classifiers into a strong classifier.

The strong classifier in each detector node is composed of multiple novelly designed two-stage weak classifiers.

Basically, boosting is an algorithm to form a strong learner by combining multiple weak learners whereby a new classifier is generated based on the result of the previously generated classifiers focusing on misclassified samples.

In the proposed solution, multiple training is avoided (only one classifier is used) and occluded weak classifiers are considered as missing data.

Rather than using all weak classifiers, we propose to use only the weak classifiers that should classify the upper part of the face (in green in the figure).

In classification stage, decision stumps are used as weak classifiers and the strong classifier is constructed as a linear combination of weak classifiers in the AdaBoost algorithm.

In the AdaBoost framework, a strong classifier consists of weak classifiers connected sequentially.