In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from...

In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, variance. It is used in supervised learning and a family of machine...

Musical ensemble Distribution ensemble or probability ensemble (cryptography) Ensemble Kalman filter Ensemble learning (statistics and machine learning) Ensembl...

In machine learning, particularly in the creation of artificial neural networks, ensemble averaging is the process of creating multiple models and combining...

Extremal Ensemble Learning (EEL) is a machine learning algorithmic paradigm for graph partitioning. EEL creates an ensemble of partitions and then uses...

Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that operates by constructing...

stochastic neighbor embedding (t-SNE) Ensemble learning AdaBoost Boosting Bootstrap aggregating (Bagging) Ensemble averaging – process of creating multiple...

machine learning approach is not enough to create an accurate estimate for certain data. Ensemble learning is a combination of several machine learning algorithms...

aggregating), is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical...

retrieval, bioinformatics, data compression, computer graphics and machine learning. Pattern recognition has its origins in statistics and engineering; some...