Real-time Learning when Concepts Shift

摘要

We are interested in real-time learning problems where the underlying stochastic process, which generates the target concept, changes over time. We want our learner to detect when a change has occurred, thus realizing that the learned concept no longer fits the observed data. Our initial approach to this problem has been to analyze offline methods for addressing concept shifts and to apply them to real-time problems. This work involves the application of the Minimum Description Length principle to detecting real-time concept shifts.