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Machine Learning for Cloud Management explores cloud resource management through predictive modelling and virtual machine placement. The book is ideal for researchers who are working in the domain of cloud computing.
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This edited collection describes recent progress on lazy learning, a branch of machine learning concerning algorithms that defer the processing of their inputs, reply to information requests by combining stored data, and typically discard constructed replies.
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This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while... Læs mere
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The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually... Læs mere
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This open access book focuses on robot introspection, which has a direct impact on physical human–robot interaction and long-term autonomy, and which can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies.
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It covers a wide range of methods and technologies, including deep neural networks, large-scale neural models, brain–computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more.
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Major updates on techniques and subject coverage (including deep learning) are included. Topics and features: guides the... Læs mere
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Introduces fundamental theory, basic and advanced algorithms, and system design issues. Essential reading for experienced researchers and developers, or for those who are just entering the field.
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Metaheuristic Algorithms in Industry 4.0 provides a guiding light to engineers, researchers, students, faculty and other professionals engaged in exploring and implementing industry 4.0 solutions in various systems and processes.