Central Library

Normal view MARC view ISBD view

Artificial Adaptive Systems Using Auto Contractive Maps [electronic resource] : Theory, Applications and Extensions / by Paolo Massimo Buscema, Giulia Massini, Marco Breda, Weldon A. Lodwick, Francis Newman, Masoud Asadi-Zeydabadi.

By: Buscema, Paolo Massimo [author.].
Contributor(s): Massini, Giulia [author.] | Breda, Marco [author.] | Lodwick, Weldon A [author.] | Newman, Francis [author.] | Asadi-Zeydabadi, Masoud [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Systems, Decision and Control: 131Publisher: Cham : Springer International Publishing : Imprint: Springer, 2018Edition: 1st ed. 2018.Description: VII, 179 p. 97 illus., 74 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319750491.Subject(s): Computational intelligence | Data mining | Artificial intelligence | Mathematical logic | Computational Intelligence | Data Mining and Knowledge Discovery | Artificial Intelligence | Mathematical Logic and FoundationsAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
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.
In: Springer Nature eBookSummary: 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.
    average rating: 0.0 (0 votes)
No physical items for this record

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.

There are no comments for this item.

Log in to your account to post a comment.