Abstract: A sensor data processor is described comprising a memory storing a plurality of trained expert models. The machine learning system has a processor configured to receive an unseen sensor data example and, for each trained expert model, compute a prediction from the unseen sensor data example using the trained expert model. The processor is configured to aggregate the predictions to form an aggregated prediction, receive feedback about the aggregated prediction and update, for each trained expert, a weight associated with that trained expert, using the received feedback. The processor is configured to compute a second aggregated prediction by computing an aggregation of the predictions which takes into account the weights.

Abstract: A method of training a random decision tree to give improved generalization ability is described. At a split node of the random decision tree a plurality of training sensor data elements available at the split node are divided into a tuning set and a validation set. A plurality of models is formed using the tuning set, each model using different values of parameters of the split node. Performance of the models at splitting the validation set between left and right child nodes of the split node is computed and used to select one of the models.