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?
