Ensemble averaging – process of creating multiple models and combining them to produce a desired output, as opposed to creating just one model. Frequently an ensemble of models performs better than any individual model, because the various errors of the models "average out."
Active learning – special case of semi-supervised learning in which a learning algorithm is able to interactively query the user (or some other information source) to obtain the desired outputs at new data points.[5][6]
Popular online course by
Andrew Ng, at
Coursera. It uses
GNU Octave. The course is a free version of
Stanford University's actual course taught by Ng, see.stanford.edu/Course/CS229 available for free].
mloss is an academic database of open-source machine learning software.
Ensemble averaging – process of creating multiple models and combining them to produce a desired output, as opposed to creating just one model. Frequently an ensemble of models performs better than any individual model, because the various errors of the models "average out."
Active learning – special case of semi-supervised learning in which a learning algorithm is able to interactively query the user (or some other information source) to obtain the desired outputs at new data points.[5][6]
Popular online course by
Andrew Ng, at
Coursera. It uses
GNU Octave. The course is a free version of
Stanford University's actual course taught by Ng, see.stanford.edu/Course/CS229 available for free].
mloss is an academic database of open-source machine learning software.