Special Session on "Adaptive Learning in Non-Stationary Environments"
Sprache des Titels:
The computerization of many life activities and the advances in data collection and storage technology lead to obtain mountains of data. They are collected to capture information about a phenomena or a
process behavior. These data are rarely of direct benefit. Thus, a set of techniques and tools are used to extract useful information for decision support, prediction, exploration and understanding of
phenomena governing the data sources. The information is mostly provided in terms of system models describing the behaviour of the
actual system or application under examination.
Whenever dynamic process changes occur due to changing system states, varying operation modes, or environmental conditions, the information
content extracted from older (batch off-line) data sources
needs to be adjusted; otherwise, the models may deteriorate significantly in performance. In on-line
settings, this circumstance requires permanent updates of
model components and parameters, in off-line applications a transfer of old models to new states.