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

Advances in self-organizing maps and...

Merenyi, Erzsebet.

FindBook

Google Book

Amazon

博客來

Advances in self-organizing maps and learning vector quantization = proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016 /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advances in self-organizing maps and learning vector quantization/ edited by Erzsebet Merenyi, Michael J. Mendenhall, Patrick O'Driscoll.
其他題名:	proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016 /
其他作者:	Merenyi, Erzsebet.
出版者:	Cham :Springer International Publishing : : 2016.,
面頁冊數:	xiii, 370 p. :ill., digital ;24 cm.
內容註:	Self-Organizing Map Learning, Visualization, and Quality Assessment -- Clustering and Time Series Analysis with Self-Organizing Maps and Neural Gas -- Applications in Control, Planning, and Dimensionality Reduction, and Hardware for Self-Organizing Maps -- Self-Organizing Maps in Neuroscience and Medical Applications -- Learning Vector Quantization Theories and Applications I -- Learning Vector Quantization Theories and Applications II.
Contained By:	Springer eBooks
標題:	Neural networks (Computer science) -
電子資源:	http://dx.doi.org/10.1007/978-3-319-28518-4
ISBN:	9783319285184$q(electronic bk.)

Advances in self-organizing maps and learning vector quantization = proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016 /
Advances in self-organizing maps and learning vector quantizationproceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016 /[electronic resource] :edited by Erzsebet Merenyi, Michael J. Mendenhall, Patrick O'Driscoll. - Cham :Springer International Publishing :2016. - xiii, 370 p. :ill., digital ;24 cm. - Advances in intelligent systems and computing,v.4282194-5357 ;. - Advances in intelligent systems and computing ;173..

Self-Organizing Map Learning, Visualization, and Quality Assessment -- Clustering and Time Series Analysis with Self-Organizing Maps and Neural Gas -- Applications in Control, Planning, and Dimensionality Reduction, and Hardware for Self-Organizing Maps -- Self-Organizing Maps in Neuroscience and Medical Applications -- Learning Vector Quantization Theories and Applications I -- Learning Vector Quantization Theories and Applications II.

This book contains the articles from the international conference 11th Workshop on Self-Organizing Maps 2016 (WSOM 2016), held at Rice University in Houston, Texas, 6-8 January 2016. WSOM is a biennial international conference series starting with WSOM'97 in Helsinki, Finland, under the guidance and direction of Professor Tuevo Kohonen (Emeritus Professor, Academy of Finland) WSOM brings together the state-of-the-art theory and applications in Competitive Learning Neural Networks: SOMs, LVQs and related paradigms of unsupervised and supervised vector quantization. The current proceedings present the expert body of knowledge of 93 authors from 15 countries in 31 peer reviewed contributions. It includes papers and abstracts from the WSOM 2016 invited speakers representing leading researchers in the theory and real-world applications of Self-Organizing Maps and Learning Vector Quantization: Professor Marie Cottrell (Universite Paris 1 Pantheon Sorbonne, France), Professor Pablo Estevez (University of Chile and Millennium Instituteof Astrophysics, Chile), and Professor Risto Miikkulainen (University of Texas at Austin, USA) The book comprises a diverse set of theoretical works on Self-Organizing Maps, Neural Gas, Learning Vector Quantization and related topics, and an excellent variety of applications to data visualization, clustering, classification, language processing, robotic control, planning, and to the analysis of astronomical data, brain images, clinical data, time series, and agricultural data.

ISBN: 9783319285184$q(electronic bk.)

Standard No.: 10.1007/978-3-319-28518-4doiSubjects--Topical Terms:

532070
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

Advances in self-organizing maps and learning vector quantization = proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016 /
LDR:03131nmm a2200325 a 4500 001 2030338
003 DE-He213
005 20160819120834.0
006 m d
007 cr nn 008maaau
008 160908s2016 gw s 0 eng d
020 $a 9783319285184$q(electronic bk.)
020 $a 9783319285177$q(paper)
024 7 $a 10.1007/978-3-319-28518-4 $2 doi
035 $a 978-3-319-28518-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.87
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.32 $2 23
090 $a QA76.87 $b .A244 2016
245 1 0 $a Advances in self-organizing maps and learning vector quantization $h [electronic resource] : $b proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016 / $c edited by Erzsebet Merenyi, Michael J. Mendenhall, Patrick O'Driscoll.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xiii, 370 p. : $b ill., digital ; $c 24 cm.
490 1 $a Advances in intelligent systems and computing, $x 2194-5357 ; $v v.428
505 0 $a Self-Organizing Map Learning, Visualization, and Quality Assessment -- Clustering and Time Series Analysis with Self-Organizing Maps and Neural Gas -- Applications in Control, Planning, and Dimensionality Reduction, and Hardware for Self-Organizing Maps -- Self-Organizing Maps in Neuroscience and Medical Applications -- Learning Vector Quantization Theories and Applications I -- Learning Vector Quantization Theories and Applications II.
520 $a This book contains the articles from the international conference 11th Workshop on Self-Organizing Maps 2016 (WSOM 2016), held at Rice University in Houston, Texas, 6-8 January 2016. WSOM is a biennial international conference series starting with WSOM'97 in Helsinki, Finland, under the guidance and direction of Professor Tuevo Kohonen (Emeritus Professor, Academy of Finland) WSOM brings together the state-of-the-art theory and applications in Competitive Learning Neural Networks: SOMs, LVQs and related paradigms of unsupervised and supervised vector quantization. The current proceedings present the expert body of knowledge of 93 authors from 15 countries in 31 peer reviewed contributions. It includes papers and abstracts from the WSOM 2016 invited speakers representing leading researchers in the theory and real-world applications of Self-Organizing Maps and Learning Vector Quantization: Professor Marie Cottrell (Universite Paris 1 Pantheon Sorbonne, France), Professor Pablo Estevez (University of Chile and Millennium Instituteof Astrophysics, Chile), and Professor Risto Miikkulainen (University of Texas at Austin, USA) The book comprises a diverse set of theoretical works on Self-Organizing Maps, Neural Gas, Learning Vector Quantization and related topics, and an excellent variety of applications to data visualization, clustering, classification, language processing, robotic control, planning, and to the analysis of astronomical data, brain images, clinical data, time series, and agricultural data.
650 0 $a Neural networks (Computer science) $3 532070
650 0 $a Self-organizing maps $v Congresses. $3 2071860
650 0 $a Self-organizing systems $v Congresses. $3 1532624
650 1 4 $a Engineering. $3 586835
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
700 1 $a Merenyi, Erzsebet. $3 2181861
700 1 $a Mendenhall, Michael J. $3 2181862
700 1 $a O'Driscoll, Patrick. $3 2181863
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Advances in intelligent systems and computing ; $v 173. $3 1619861
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-28518-4
950 $a Engineering (Springer-11647)