AHaH Computing–From Metastable Switches to Attractors to Machine Learning
For the first 30% of samples from the Reuters-21578 data set, the AHaH classifier was operated in supervised mode followed by operation in unsupervised mode for the remaining samples. A confidence threshold of 1.0 was set for unsupervised application of a learn signal. The F1 score for the top ten most frequently occurring labels in the Reuters-21578 data set were tracked. These results show that the AHaH classifier is capable of continuously improving its performance without supervised feedback.