This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was proposed by William W. Cohen […]
Class implementing the Cobweb and Classit clustering algorithms. Note: the application of node operators (merging, splitting etc.) in terms of ordering and […]
Simple EM (expectation maximisation) class. EM assigns a probability distribution to each instance which indicates the probability of it belonging to each […]
Reads a TensorFlow 2 network from a file or directory.
Writes a TensorFlow 2 Network to a file or directory.
A metaclassifier for handling multi-class datasets with 2-class classifiers. This classifier is also capable of applying error correcting output codes for […]
Class for bagging a classifier to reduce variance. Can do classification and regression depending on the base learner. For more information, see Leo […]
Implements Winnow and Balanced Winnow algorithms by Littlestone. For more information, see N. Littlestone (1988). Learning quickly when irrelevant […]
Cluster data using the FarthestFirst algorithm. For more information see: Hochbaum, Shmoys (1985). A best possible heuristic for the k-center problem. […]
A meta classifier for handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies. For more info, check Lin Dong, […]