1st Edition
by Steven J. Simske (Author)
The confluence of cloud computing,
parallelism and advanced machine intelligence approaches has created a
world in which the optimum knowledge system will usually be architected
from the combination of two or more knowledge-generating systems. There
is a need, then, to provide a reusable, broadly-applicable set of design
patterns to empower the intelligent system architect to take advantage
of this opportunity.
This book explains how to design and build
intelligent systems that are optimized for changing system requirements
(adaptability), optimized for changing system input (robustness), and
optimized for one or more other important system parameters (e.g.,
accuracy, efficiency, cost). It provides an overview of traditional
parallel processing which is shown to consist primarily of task and
component parallelism; before introducing meta-algorithmic parallelism
which is based on combining two or more algorithms, classification
engines or other systems.
Key features:
- Explains the
entire roadmap for the design, testing, development, refinement,
deployment and statistics-driven optimization of building systems for
intelligence
- Offers an accessible yet thorough overview of machine intelligence, in addition to having a strong image processing focus
- Contains
design patterns for parallelism, especially meta-algorithmic
parallelism – simply conveyed, reusable and proven effective that can be
readily included in the toolbox of experts in analytics, system
architecture, big data, security and many other science and engineering
disciplines
- Connects algorithms and analytics to parallelism,
thereby illustrating a new way of designing intelligent systems
compatible with the tremendous changes in the computing world over the
past decade
- Discusses application of the approaches to a wide
number of fields; primarily, document understanding, image
understanding, biometrics and security printing
- Companion website contains sample code and data sets