東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Search techniques in intelligent cla...

Savchenko, Andrey V.

FindBook

Google Book

Amazon

博客來

Search techniques in intelligent classification systems

紀錄類型:	書目-電子資源 : Monograph/item
正題名/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.
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.
Contained By:	Springer eBooks
標題:	Databases. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-30515-8
ISBN:	9783319305158

Search techniques in intelligent classification systems
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:

747532
Databases.

LC Class. No.: QA76.9.D3

Dewey Class. No.: 005.74

Search techniques in intelligent classification systems
LDR:02802nmm a2200325 a 4500 001 2038281
003 DE-He213
005 20161102093100.0
006 m d
007 cr nn 008maaau
008 161209s2016 gw s 0 eng d
020 $a 9783319305158 $q (electronic bk.)
020 $a 9783319305134 $q (paper)
024 7 $a 10.1007/978-3-319-30515-8 $2 doi
035 $a 978-3-319-30515-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D3
072 7 $a PBU $2 bicssc
072 7 $a MAT003000 $2 bisacsh
082 0 4 $a 005.74 $2 23
090 $a QA76.9.D3 $b S266 2016
100 1 $a Savchenko, Andrey V. $3 2195370
245 1 0 $a Search techniques in intelligent classification systems $h [electronic resource] / $c by Andrey V. Savchenko.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xiii, 82 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a SpringerBriefs in optimization, $x 2190-8354
505 0 $a 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.
520 $a 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?
650 0 $a Databases. $3 747532
650 0 $a Database searching. $3 577808
650 0 $a Data mining. $3 562972
650 0 $a Automatic classification. $3 1569653
650 1 4 $a Mathematics. $3 515831
650 2 4 $a Optimization. $3 891104
650 2 4 $a Pattern Recognition. $3 891045
650 2 4 $a Machinery and Machine Elements. $3 893855
650 2 4 $a Systems Theory, Control. $3 893834
650 2 4 $a Complex Systems. $3 1566441
650 2 4 $a Potential Theory. $3 893956
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a SpringerBriefs in optimization. $3 1566137
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-30515-8
950 $a Mathematics and Statistics (Springer-11649)