Meta-Algorithmics (PDF)

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