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This book is a timely collection of chapters that present the state of the art within the analysis and application of big data.
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At the centre of the methodology used in this book is STEM learning variability space that includes STEM pedagogical variability, learners’ social variability, technological variability, CS content variability and interaction variability.
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This book features both cutting-edge contributions on managing knowledge in transformational contexts and a selection of real-world case studies.
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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.
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This unique book succinctly summarizes the need to measure how ontologies (one of the building blocks of the Semantic Web) are currently being utilized, providing insights for various stakeholders.
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This edited volume presents examples of social science research projects that employ new methods of quantitative analysis and mathematical modeling of social processes.
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The purpose of this book is to review the recent advances in E-health technologies and applications.
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The notions of mutual reinforcement or redundancy are modeled explicitly through coefficients of fuzzy measures, and fuzzy integrals, such as the Choquet and Sugeno integrals combine the inputs.
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This book presents the complex topic of using computational intelligence for pattern recognition in a straightforward and applicable way, using Matlab to illustrate topics and concepts.
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Statistical learning theory answers these questions by deriving non-asymptotic bounds on the generalization error of a model or, in other words, by upper bounding the true error of the learned model based just on quantities computed on the available data.
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