The easiest way to use the wrapper is using train, but beforehand you need the data to be used contained in a DMatrix object This is a work in progress, feedback, comments, issues, suggestion …

Creation of AI::NaiveBayes classifier object out of training data is done by AI::NaiveBayes::Learner. For quick start you can use the limited train class method that trains the classifier in a default way. The …

The second argument is the number of iterations to train the set. If this argument is not provided here, you may use the iterations() method to set it (prior to calling train_set(), of course).

Two classes are used: AI::FANN that wraps the C struct fann type and AI::FANN::TrainData that wraps struct fann_train_data. Prefixes and common parts on the C function names referring to those …

The model file should be of the format produced by the svm-train program (distributed with the libsvm library) or from the $svm->save () method. New SVM's can be created using the following parameters:

DESCRIPTION The CPAN module automates or at least simplifies the make and install of perl modules and extensions. It includes some primitive searching capabilities and knows how to use LWP, …

Note: when using a ramp () activatior, train the net at least TWICE on the data set, because the first time the ramp () function searches for the top value in the inputs, and therefore, results could flucuate.

If you plan to use the DataSource to train an MLModel, the DataSource also needs a recipe. A recipe describes how each input variable will be used in training an MLModel.

* Original paper is here: <http://davheld.github.io/GOTURN/GOTURN.pdf> * As long as original authors implementation: <https://github.com/davheld/GOTURN#train-the-tracker> * Implementation of …

train This method computes the start, emission and transition probabilities from a set of observations and their associated states. The probabilities are simple averages of the passed observations, so if …