Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation (Adaptive Computation and Machine Learning)

This book provides a comprehensive overview of machine learning techniques for dealing with non-stationarity in various applications, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics.

The authors present state-of-the-art research on learning under covariate shift, model selection, importance estimation, and active learning, as well as real-world applications of covariate shift adaptation such as brain-computer interface, speaker identification, and age prediction from facial images.

With this book, the authors aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.