Sentence examples for many simple classifiers from inspiring English sources

The idea of aggregating many simple classifiers to yield a better classifier is a widely used strategy in machine learning that capitalizes on the idea that using a set of classifiers that produce barely better results than random guessing can achieve arbitrarily high accuracy when combined appropriately.

To optimize the construct of an ensemble of many relatively small and simple classifiers may be more realistic than to optimize the design of an individual large and complex classifier.

Classifier ensemble methods and simple classifiers were examined.

There are many, simple ways.

Our results reinforce the claims from recent literature that classifier ensemble methods specifically designed for imbalanced problems have substantial advantages over simple classifiers and standard classifier ensembles.

The cascade classifier achieves both high processing speed and detection performance by employing simple classifiers at early stages to reject non-objects and complex classifiers at later stages.

These approaches group the trajectories of moving objects using simple classifiers such as K-means.

The ensemble classifier usually performs better than simple classifiers [ 53].

The 10-fold cross-validation results of ensemble classifier LibD3C and simple classifiers are shown in Figure 11.

The central idea is to create a complex classifier from a population of weak or simple classifiers.

Furthermore, simple classifiers seem to perform remarkably well when compared to more sophisticated ones [ 4].