NON-GREEDY MACHINE LEARNING FOR HIGH ACCURACY

摘要

Non-greedy machine learning for high accuracy is described, for example, where one or more random decision trees are trained for gesture recognition in order to control a computing-based device. In various examples, a random decision tree or directed acyclic graph (DAG) is grown using a greedy process and is then post-processed to recalculate, in a non-greedy process, leaf node parameters and split function parameters of internal nodes of the graph. In various examples the very large number of options to be assessed by the non-greedy process is reduced by using a constrained objective function. In examples the constrained objective function takes into account a binary code denoting decisions at split nodes of the tree or DAG. In examples, resulting trained decision trees are more compact and have improved generalization and accuracy.

主权项

1. A computer-implemented method comprising:
receiving, at a processor, an unseen example; applying the unseen example to a trained machine learning system, the machine learning system having been trained using training data comprising pairs of training examples and ground truth data, in a non-greedy process, to predict values associated with future examples; a non-greedy process being a process which considers a total number of choices; and using the predicted values to control a computing device.