Ensemble Learning
A number of classifiers are strategically generated and combined to solve a particular computational intelligence problem. Given a set of training examples, a learning algorithm outputs a classifier which is an hypothesis about the true function F that generates the label values Y from the input sample values X. Given new X values, the classifier predicts the corresponding Y values.
Goals:
Improve the (classification, prediction, function approximation, etc.) performance of a model, or reduce the likelihood of an unfortunate selection of a poor one.
BrandIdea’s Implementation:
Handwriting recognition, Image processing, Image segmentation.