A unified methodology for categorizing various complex
objects is presented in this book. Through probability theory, novel
asymptotically minimax criteria suitable for practical applications in imaging
and data analysis are examined including the special cases such as the
Jensen-Shannon divergence and the probabilistic neural network. An optimal
approximate nearest neighbor search algorithm, which allows faster
classification of databases is featured. Rough set theory, sequential analysis
and granular computing are used to improve performance of the hierarchical
classifiers. Practical examples in face identification (including deep neural
networks), isolated commands recognition in voice control system and
classification of visemes captured by the Kinect depth camera are included.
This approach creates fast and accurate search procedures by using exact
probability densities of applied dissimilarity measures.

This
book can be used as a guide for independent study and as supplementary material
for a technically oriented graduate course in intelligent systems and data
mining. Students and researchers interested in the theoretical and practical
aspects of intelligent classification systems will find answers to:


- Why conventional implementation of the naive Bayesian
approach does not work well in image classification?

- How to deal with insufficient performance of hierarchical
classification systems?







- Is it possible to prevent an exhaustive search of the
nearest neighbor in a database?