Inductive transfer and machine life-long learning

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Machine learning research has focused on inducing a model of a function or classification task strictly from a set of training examples. Consequently, machine learning systems do not take advantage of previously acquired task knowledge when learning a new and potentially related task. This is unfortunate since prior task knowledge offers the potential to produce more accurate models in less time and using fewer training examples. My research investigates machine life-long learning (ML3) systems (particularly, architectures of artificial neural networks) that are capable of retaining and selectively transferring task knowledge when learning a new task. Data mining and adaptive software systems can benefit from the use of inductive transfer and ML3 methods. For example, more accurate medical diagnostic models can be developed from small samples of patients by using previously learned and related diagnostic models. Adaptive systems can learn more efficiently the interaction model (profile) of a new user by selectively transferring knowledge from similar user models.