Title: "Learning in Nonstationary Environments: Perspectives and Applications"

Abstract: Many machine­learning techniques make the assumption that training and testing data are sampled from the same probability distribution. Unfortunately, in an increasing number of real-world learning scenarios data arrive in a stream, and the probabilistic properties of the data generating process might be changing with time, violating the above assumption. Any algorithm or model that does not account for such change is almost certainly going to fail when data are sampled from a drifting or changing distribution, i.e, non stationary environment (NSE). The problem of learning in NSE has drawn much attentionin the last few years, particularly, in the classification literature where the problem is typically referred to as learning underconcept drift. Learning in NSE is a challenging problem because concept drift occurs unpredictably, and may change the data­ generating process into an unforeseen state. The literature boasts algorithms for learning in NSE, which can be though divided in two main learning strategies: (a) undergoing continuous adaptation to match the recent concept (passive approach), or (b) steadily monitoring the data stream to detect concept drift and eventually react (active approaches).