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Artificial Adaptive Systems Using Auto Contractive Maps, Paolo Massimo Buscema; Giulia Massini; Marco Breda


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Цена: 14635.00р.
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Автор: Paolo Massimo Buscema; Giulia Massini; Marco Breda
Название:  Artificial Adaptive Systems Using Auto Contractive Maps
ISBN: 9783030091354
Издательство: Springer
Классификация:




ISBN-10: 303009135X
Обложка/Формат: Soft cover
Страницы: 179
Вес: 0.30 кг.
Дата издания: 2018
Серия: Studies in Systems, Decision and Control
Язык: English
Издание: Softcover reprint of
Иллюстрации: 100 tables, color; 74 illustrations, color; 23 illustrations, black and white; vii, 179 p. 97 illus., 74 illus. in color.
Размер: 234 x 156 x 10
Читательская аудитория: General (us: trade)
Основная тема: Engineering
Подзаголовок: Theory, Applications and Extensions
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: 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.

Дополнительное описание: 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.



Artificial Adaptive Systems Using Auto Contractive Maps

Автор: Buscema
Название: Artificial Adaptive Systems Using Auto Contractive Maps
ISBN: 3319750488 ISBN-13(EAN): 9783319750484
Издательство: Springer
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Цена: 12196.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: 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.

Innovations and Approaches for Resilient and Adaptive Systems

Автор: Vincenzo De Florio
Название: Innovations and Approaches for Resilient and Adaptive Systems
ISBN: 1466620560 ISBN-13(EAN): 9781466620568
Издательство: Mare Nostrum (Eurospan)
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Цена: 28413.00 р.
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Описание: Provides a comprehensive collection of knowledge on increasing the notions and models in adaptive and dependable systems. This book aims to enhance the awareness of the role of adaptability and resilience in system environments for researchers, practitioners, educators, and professionals alike.

Adaptive Micro Learning - Using Fragmented Time To Learn

Автор: Sun Geng, Shen Jun, Lin Jiayin
Название: Adaptive Micro Learning - Using Fragmented Time To Learn
ISBN: 9811207453 ISBN-13(EAN): 9789811207457
Издательство: World Scientific Publishing
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Цена: 11088.00 р.
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Описание: This compendium introduces an artificial intelligence-supported solution to realize adaptive micro learning over open education resource (OER). The advantages of cloud computing and big data are leveraged to promote the categorization and customization of OERs micro learning context. For a micro-learning service, OERs are tailored into fragmented pieces to be consumed within shorter time frames.Firstly, the current status of mobile-learning, micro-learning, and OERs are described. Then, the significances and challenges of Micro Learning as a Service (MLaaS) are discussed. A framework of a service-oriented system is provided, which adopts both online and offline computation domain to work in conjunction to improve the performance of learning resource adaptation.In addition, a comprehensive learner model and a knowledge base is prepared to semantically profile the learners and learning resource. The novel delivery and access mode of OERs suffers from the cold start problem because of the shortage of already-known learner information versus the continuously released new micro OERs. This unique volume provides an excellent feasible algorithmic solution to overcome the cold start problem.


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