Learning in Non-Stationary Environments: Methods and Applications

This book treats the problem of learning from data issued of time/spatial-based complex non-stationary processes. It presents efficient techniques, methods and tools able to manage, to exploit and to interpret correctly the increasing amount of data in environments that are continuously changing. The goal is to build a model for predicting and classifying the future system behavior, able to tackle and to govern the high variability of complex non-stationary and large-scale systems.This book addresses the problems of modeling, prediction, classification, data understanding and processing in non-stationary and unpredictable environments. It presents recent methods and approaches for the design of systems able to learn and to fully adapt its structure and to adjust its parameters according to the changes in their environments. This book includes various applications such as: medicine diagnosis, robotics, business, industrial control, fault detection and diagnosis, quality control, surface inspection, system identification, transportation, communications, web applications, environmental monitoring, biomedical systems, decision support systems, security, and electronic services.