Artificial Adaptive Systems Using Auto Contractive Maps, Paolo Massimo Buscema; Giulia Massini; Marco Breda
Автор: Buscema Название: Artificial Adaptive Systems Using Auto Contractive Maps ISBN: 3319750488 ISBN-13(EAN): 9783319750484 Издательство: Springer Рейтинг: 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.
Автор: Vincenzo De Florio Название: Innovations and Approaches for Resilient and Adaptive Systems ISBN: 1466620560 ISBN-13(EAN): 9781466620568 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 28413.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
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 Рейтинг: Цена: 11088.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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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