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Artificial adaptive systems using au...

Buscema, Paolo Massimo.

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Artificial adaptive systems using auto contractive maps = theory, applications and extensions /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Artificial adaptive systems using auto contractive maps/ by Paolo Massimo Buscema ... [et al.].
其他題名:	theory, applications and extensions /
其他作者:	Buscema, Paolo Massimo.
出版者:	Cham :Springer International Publishing : : 2018.,
面頁冊數:	vii, 179 p. :ill. (some col.), digital ;24 cm.
內容註:	An Introduction -- Artificial Neural Networks -- Auto-Contractive Maps -- Visualization of Auto-CM Output -- Dataset Transformations and Auto-CM -- Comparison of Auto-CM to Various Other Data Understanding Approaches.
Contained By:	Springer eBooks
標題:	Adaptive computing systems. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-75049-1
ISBN:	9783319750491

Artificial adaptive systems using auto contractive maps = theory, applications and extensions /
Artificial adaptive systems using auto contractive mapstheory, applications and extensions /[electronic resource] :by Paolo Massimo Buscema ... [et al.]. - Cham :Springer International Publishing :2018. - vii, 179 p. :ill. (some col.), digital ;24 cm. - Studies in systems, decision and control,v.1312198-4182 ;. - Studies in systems, decision and control ;v.131..

An Introduction -- Artificial Neural Networks -- Auto-Contractive Maps -- Visualization of Auto-CM Output -- Dataset Transformations and Auto-CM -- Comparison of Auto-CM to Various Other Data Understanding Approaches.

This book offers an introduction to artificial adaptive systems and a general model of the relationships between the data and algorithms used to analyze them. It subsequently describes artificial neural networks as a subclass of artificial adaptive systems, and reports on the backpropagation algorithm, while also identifying an important connection between supervised and unsupervised artificial neural networks. The book's primary focus is on the auto contractive map, an unsupervised artificial neural network employing a fixed point method versus traditional energy minimization. This is a powerful tool for understanding, associating and transforming data, as demonstrated in the numerous examples presented here. A supervised version of the auto contracting map is also introduced as an outstanding method for recognizing digits and defects. In closing, the book walks the readers through the theory and examples of how the auto contracting map can be used in conjunction with another artificial neural network, the "spin-net," as a dynamic form of auto-associative memory.

ISBN: 9783319750491

Standard No.: 10.1007/978-3-319-75049-1doiSubjects--Topical Terms:

628890
Adaptive computing systems.

LC Class. No.: QA76.9.A3

Dewey Class. No.: 004

Artificial adaptive systems using auto contractive maps = theory, applications and extensions /
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505 0 $a An Introduction -- Artificial Neural Networks -- Auto-Contractive Maps -- Visualization of Auto-CM Output -- Dataset Transformations and Auto-CM -- Comparison of Auto-CM to Various Other Data Understanding Approaches.
520 $a This book offers an introduction to artificial adaptive systems and a general model of the relationships between the data and algorithms used to analyze them. It subsequently describes artificial neural networks as a subclass of artificial adaptive systems, and reports on the backpropagation algorithm, while also identifying an important connection between supervised and unsupervised artificial neural networks. The book's primary focus is on the auto contractive map, an unsupervised artificial neural network employing a fixed point method versus traditional energy minimization. This is a powerful tool for understanding, associating and transforming data, as demonstrated in the numerous examples presented here. A supervised version of the auto contracting map is also introduced as an outstanding method for recognizing digits and defects. In closing, the book walks the readers through the theory and examples of how the auto contracting map can be used in conjunction with another artificial neural network, the "spin-net," as a dynamic form of auto-associative memory.
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