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Solving Large Scale Learning Tasks. Challenges and Algorithms

researchers with whom she collaborated. The 23 refereed articles in this Festschrift volume provide challenges and solutions from;

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Large-Scale Scientific Computing

of metaheuristics to large-scale problems; large scale machine learning: multiscale algorithms and performance guarantees; and contributed papers.;

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Distributed Machine Learning and Gradient Optimization

big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning;

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Pattern Recognition Algorithms for Data Mining

concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with;

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Pattern Recognition Algorithms for Data Mining

concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with;

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Advances in High Performance Computing

effort and to maximize the achieved profit. Many challenging real world problems arising in engineering, economics, medicine and other;

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Quantum Machine Learning

Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving a classical machine;

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Broad Learning Through Fusions

This book offers a clear and comprehensive introduction to broad learning, one of the novel learning problems studied in data mining and;

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Broad Learning Through Fusions

This book offers a clear and comprehensive introduction to broad learning, one of the novel learning problems studied in data mining and;

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Applications of Big Data in Large- and Small-Scale Systems

demand for big data analytics that offer insights for computing efficiency, knowledge discovery, problem solving, and event prediction. To handle;

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Applications of Big Data in Large- and Small-Scale Systems

demand for big data analytics that offer insights for computing efficiency, knowledge discovery, problem solving, and event prediction. To handle;

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Algorithms for Large Scale Linear Algebraic Systems:

An overview of the most successful algorithms and techniques for solving large, sparse systems of equations and some algorithms and;

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Intelligent Random Walk

social networks. The book opens new horizons for designing prediction models and problem-solving methods based on intelligent random walk;

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Deep Learning and Convolutional Neural Networks for Medical Image Computing: Precision Medicine, High Performance and Large-Scale Datasets

This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation;

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Medical Computer Vision: Algorithms for Big Data

of anomalies emphasizing questions of harvesting, organizing and learning from large-scale medical imaging data sets and general-purpose automatic;

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Combinatorial Scientific Computing

optimization, algorithmic differentiation of numerical simulation code, sparse matrix software tools, and combinatorial challenges and applications;

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Combinatorial Scientific Computing

optimization, algorithmic differentiation of numerical simulation code, sparse matrix software tools, and combinatorial challenges and applications;

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Entropy Guided Transformation Learning

Entropy Guided Transformation Learning: Algorithms and Applications (ETL) presents a machine learning algorithm for classification tasks;

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Big Data

consumer applications. To help realize Big Data's full potential, the book addresses numerous challenges, offering the conceptual and;

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Learning Decision Sequences For Repetitive Processes-Selected Algorithms

This book provides tools and algorithms for solving a wide class of optimization tasks by learning from their repetitions. A unified;

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Optimization, Learning, and Control for Interdependent Complex Networks

This book focuses on a wide range of optimization, learning, and control algorithms for interdependent complex networks and their role;

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Assessing Deeper Learning

Deeper learning has been defined as the skills and knowledge that students must possess to succeed in 21st century jobs and civic life;

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Python

is expected. Machine learning basic knowledge is also expected. What You Will Learn * Use predictive modeling and apply it to real-world problems;

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Machine Learning for Adaptive Many Core Machines A Practical Approach

range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML;

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Deep Learning and Convolutional Neural Networks for Medical Image Computing

This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation;

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Optimization Theory for Large Syste

in problem-solving. Initial chapter on linear and nonlinear programming presents all necessary background for subjects covered in rest of book;

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Large-Scale Conservation in the Common Interest

authors argue that a more holistic and genuinely interdisciplinary approach is required to solve the complex and growing challenges associated;

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