Recent decades have seen rapid advances in automatization
processes, supported by modern machines and computers. The result
is significant increases in system complexity and state changes,
information sources, the need for faster data handling and the
integration of environmental influences. Intelligent systems,
equipped with a taxonomy of data-driven system identification and
machine learning algorithms, can handle these problems partially.
Conventional learning algorithms in a batch off-line setting fail
whenever dynamic changes of the process appear due to
non-stationary environments and external influences.
"Learning in Non-Stationary Environments: Methods and
Applications "offers a wide-ranging, comprehensive review of recent
developments and important methodologies in the field. The coverage
focuses on dynamic learning in unsupervised problems, dynamic
learning in supervised classification and dynamic learning in
supervised regression problems. A later section is dedicated to
applications in which dynamic learning methods serve as keystones
for achieving models with high accuracy.
Rather than rely on a mathematical theorem/proof style, the
editors highlight numerous figures, tables, examples and
applications, together with their explanations.
This approach offers a useful basis for further investigation
and fresh ideas and motivates and inspires newcomers to explore
this promising and still emerging field of research.
"
General
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!