Search Techniques in Intelligent Classification Systems (SpringerBriefs in Optimization) - Tapa blanda

Libro 30 de 43: SpringerBriefs in Optimization

Savchenko, Andrey V. V.

 
9783319305134: Search Techniques in Intelligent Classification Systems (SpringerBriefs in Optimization)
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ISBN 10: 3319305131 ISBN 13: 9783319305134
Editorial: Springer, 2016

Sinopsis

A unified methodology for categorizing various complexobjects is presented in this book. Through probability theory, novelasymptotically minimax criteria suitable for practical applications in imagingand data analysis are examined including the special cases such as theJensen-Shannon divergence and the probabilistic neural network. An optimalapproximate nearest neighbor search algorithm, which allows fasterclassification of databases is featured. Rough set theory, sequential analysisand granular computing are used to improve performance of the hierarchicalclassifiers. Practical examples in face identification (including deep neuralnetworks), isolated commands recognition in voice control system andclassification of visemes captured by the Kinect depth camera are included.This approach creates fast and accurate search procedures by using exactprobability densities of applied dissimilarity measures.

Thisbook can be used as a guide for independent study and as supplementary materialfor a technically oriented graduate course in intelligent systems and datamining. Students and researchers interested in the theoretical and practicalaspects of intelligent classification systems will find answers to:

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

- How to deal with insufficient performance of hierarchicalclassification systems?

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

"Sinopsis" puede pertenecer a otra edición de este libro.

Reseña del editor

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?

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2016
Idioma
Inglés
ISBN 10 
3319305131
ISBN 13 
9783319305134
Encuadernación
Tapa blanda
Número de edición
1
Número de páginas
96
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