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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.
This open access book describes the results of natural language processing and machine learning methods applied to clinical text from electronic patient records. It is divided into twelve chapters.
This book features both cutting-edge contributions on managing knowledge in transformational contexts and a selection of real-world case studies.
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.
This edited volume presents examples of social science research projects that employ new methods of quantitative analysis and mathematical modeling of social processes.
This book presents the Recommender System for Improving Customer Loyalty. The data mining techniques employed in the Recommender System allow users to “learn” from the experiences of others, without sharing proprietary information.
The purpose of this book is to review the recent advances in E-health technologies and applications.
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.
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.
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.