Abstract: There is provided systems and methods for training a classifier. The method comprises: obtaining a classifier for classifying data into one of a plurality of classes; retrieving training data comprising a set of observations and a set of corresponding labels, each label representing an assigned class for a corresponding observation; and applying an agent trained by a reinforcement learning system to generate labeled data from unlabeled observations and train the classifier using the training data and the labeled data according to a policy determined by the reinforcement learning system.

Abstract: The embodiments described herein combine a number of mathematical techniques to address the problem of efficiently assessing the quality of predictions by machine learning models or explaining said predictions to a user. Influence functions are used to estimate the influence of training data points on a particular prediction made by a model in order to help explain why that prediction was justified. Through the use of influence functions, repeated retraining of the model is avoided, thereby providing a more computationally efficient means of assessing the quality of the predictions. In addition, a novel quality metric is proposed for effectively quantifying the quality of a particular prediction.

Abstract: The approach described herein provides a novel means of determining the influence of sub-components of raw input data on machine learning predictions. This is applied directly to the raw observed data, rather than to embedded data, such that the influence is determined with respect to real-world observable features that are recognizable to the user, rather than latent features that may have no meaning to the user. This is achieved without requiring retraining of the model, and therefore avoids the additional computation necessary to recalculate model parameters. This provides a simple and efficient method for determining which sub-components of the input data provide the greatest influence over the generation of individual prediction(s).

Abstract: There is provided systems and methods for training a classifier. The method comprises: obtaining a classifier for classifying data into one of a plurality of classes; retrieving training data comprising a set of observations and a set of corresponding labels, each label representing an assigned class for a corresponding observation; and applying an agent trained by a reinforcement learning system to generate labeled data from unlabeled observations and train the classifier using the training data and the labeled data according to a policy determined by the reinforcement learning system.

Abstract: There is provided a device and method for classifying data. The device comprises a controller configured to receive data, classify the data into a first class or a second class using a first machine learning classifier, and if the data is classified into the second class, classify the data into one of a third class and a fourth class using a second machine learning classifier. The first and second machine learning classifiers have their own predefined sets of rules for classifying data.

Abstract: There is provided a device and method for classifying data. The device comprises a controller configured to receive data, classify the data into a first class or a second class using a first machine learning classifier, and if the data is classified into the second class, classify the data into one of a third class and a fourth class using a second machine learning classifier. The first and second machine learning classifiers have their own predefined sets of rules for classifying data.