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Machine learning. --- Learning, Machine --- Artificial intelligence --- Machine theory

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Supervised learning (Machine learning) --- Machine learning. --- Apprentissage supervisé (intelligence artificielle) --- Apprentissage automatique.

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Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models—those that emphasize problem-specific Interpretability—with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics. Combines classical sparse and low-rank models and algorithms with the latest advances in deep learning networks Shows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning models Provides tactics on how to build and apply customized deep learning models for various applications

Beverages --- Health aspects. --- Drinks --- Potable liquids --- Potables --- Food --- Liquids --- Machine learning. --- Learning, Machine --- Artificial intelligence --- Machine theory

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This book presents the proceedings of the “Innovations in Biomedical Engineering IBE’2018” Conference held in Katowice, Poland from October 18 to 20, 2018, and discusses recent research on innovations in biomedical engineering. The book covers a broad range of subjects related to biomedical engineering innovations. Divided into four parts, it presents state-of-the-art advances in: Engineering of biomaterials, Modelling and simulations in biomechanics, Informatics in medicine, and Signal analysis. By doing so, it helps bridge the gap between technological and methodological engineering achievements on the one hand and clinical requirements in the three major areas diagnosis, therapy and rehabilitation on the other.

Computational intelligence. --- Engineering. --- Machine learning. --- Computational Intelligence. --- Machine Learning. --- Learning, Machine --- Artificial intelligence --- Machine theory --- Construction --- Industrial arts --- Technology --- Intelligence, Computational --- Soft computing

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The concepts of centrality and diversity are highly important in search algorithms, and play central roles in applications of artificial intelligence (AI), machine learning (ML), social networks, and pattern recognition. This work examines the significance of centrality and diversity in representation, regression, ranking, clustering, optimization, and classification. The text is designed to be accessible to a broad readership. Requiring only a basic background in undergraduate-level mathematics, the work is suitable for senior undergraduate and graduate students, as well as researchers working in machine learning, data mining, social networks, and pattern recognition. .

Artificial intelligence. --- Machine learning. --- Optical pattern recognition. --- Machine Learning. --- Pattern Recognition. --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Learning, Machine --- Artificial intelligence --- Machine theory --- Pattern recognition. --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception