A Pattern-Hierarchy Classifier for Reduced Teaching

by   Kieran Greer, et al.

This paper uses a branching classifier mechanism in an unsupervised scenario, to enable it to self-organise data into unknown categories. A teaching phase is then able to help the classifier to learn the true category for each input row, using a reduced number of training steps. The pattern ensembles are learned in an unsupervsised manner that use a closest-distance clustering. This is done without knowing what the actual output category is and leads to each actual category having several clusters associated with it. One measure of success is then that each of these sub-clusters is coherent, which means that every data row in the cluster belongs to the same category. The total number of clusters is also important and a teaching phase can then teach the classifier what the correct actual category is. During this phase, any classifier can also learn or infer correct classifications from some other classifier's knowledge, thereby reducing the required number of presentations. As the information is added, cross-referencing between the two structures allows it to be used more widely. With this process, a unique structure can build up that would not be possible by either method separately. The lower level is a nested ensemble of patterns created by self-organisation. The upper level is a hierarchical tree, where each end node represents a single category only, so there is a transition from mixed ensemble masses to specific categories. The structure also has relations to brain-like modelling.



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