Artificial Adaptive Systems Using Auto Contractive Maps Theory, Applications and Extensions /

• Κύριοι συγγραφείς: Buscema, Paolo Massimo (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Massini, Giulia (http://id.loc.gov/vocabulary/relators/aut), Breda, Marco (http://id.loc.gov/vocabulary/relators/aut), Lodwick, Weldon A. (http://id.loc.gov/vocabulary/relators/aut), Newman, Francis (http://id.loc.gov/vocabulary/relators/aut), Asadi-Zeydabadi, Masoud (http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Έκδοση: 1st ed. 2018.
• Σειρά: Studies in Systems, Decision and Control, 131
• Θέματα: Computational intelligence.; Data mining.; Artificial intelligence.; Mathematical logic.; Computational Intelligence.; Data Mining and Knowledge Discovery.; Artificial Intelligence.; Mathematical Logic and Foundations.

 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. 

 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.