大葉大學圖書館 |

Search techniques in intelligent classification systems[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	005.74
書名/作者:	Search techniques in intelligent classification systems/ by Andrey V. Savchenko.
作者:	Savchenko, Andrey V.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	xiii, 82 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Machinery and Machine Elements.
標題:	Systems Theory, Control.
標題:	Complex Systems.
標題:	Potential Theory.
標題:	Databases.
標題:	Database searching.
標題:	Data mining.
標題:	Automatic classification.
標題:	Mathematics.
標題:	Optimization.
標題:	Pattern Recognition.
ISBN:	9783319305158
ISBN:	9783319305134
內容註:	1.Intelligent Classification Systems -- 2. Statistical Classification of Audiovisual Data -- 3. Hierarchical Intelligent Classification Systems -- 4. Approximate Nearest Neighbor Search in Intelligent Classification Systems -- 5. Search in Voice Control Systems -- 6. Conclusion.
摘要、提要註:	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?
電子資源:	http://dx.doi.org/10.1007/978-3-319-30515-8

Search techniques in intelligent classification systems[electronic resource] /
Savchenko, Andrey V.

Search techniques in intelligent classification systems[electronic resource] /by Andrey V. Savchenko. - Cham :Springer International Publishing :2016. - xiii, 82 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in optimization,2190-8354. - SpringerBriefs in optimization..

1.Intelligent Classification Systems -- 2. Statistical Classification of Audiovisual Data -- 3. Hierarchical Intelligent Classification Systems -- 4. Approximate Nearest Neighbor Search in Intelligent Classification Systems -- 5. Search in Voice Control Systems -- 6. Conclusion.

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?

ISBN: 9783319305158

Standard No.: 10.1007/978-3-319-30515-8doiSubjects--Topical Terms:

463954
Machinery and Machine Elements.

LC Class. No.: QA76.9.D3

Dewey Class. No.: 005.74

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