Overview
- Unifies theory and practice: from statistically optimal criteria to applications in image and speech recognition
- Describes methodology of segment homogeneity testing to uniformly solve classification problems
- Contains practical aspects of modern soft computing techniques to implement fast and accurate search in intelligent systems
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Optimization (BRIEFSOPTI)
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Table of contents (6 chapters)
Keywords
- Modern intelligent systems
- Pattern Recognition
- data mining
- face identification
- probability theory
- speech recognition
- Intelligent Classification Systems
- Statistical Classification of Audiovisual Data
- Mathematical Model of the Piecewise-Regular Object
- Hierarchical Intelligent Classification Systems
- Voice Control Systems
- Nearest Neighbor Search
About this book
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?
Authors and Affiliations
Bibliographic Information
Book Title: Search Techniques in Intelligent Classification Systems
Authors: Andrey V. Savchenko
Series Title: SpringerBriefs in Optimization
DOI: https://doi.org/10.1007/978-3-319-30515-8
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Author(s) 2016
Softcover ISBN: 978-3-319-30513-4Published: 12 May 2016
eBook ISBN: 978-3-319-30515-8Published: 02 May 2016
Series ISSN: 2190-8354
Series E-ISSN: 2191-575X
Edition Number: 1
Number of Pages: XIII, 82
Number of Illustrations: 9 b/w illustrations, 19 illustrations in colour
Topics: Optimization, Pattern Recognition, Machinery and Machine Elements, Systems Theory, Control, Complex Systems, Potential Theory