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Distributed Machine Learning and Gradient Optimization

This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the;

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Optimization for Data Analysis

Optimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic;

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Regularization, Optimization, Kernels, and Support Vector Machines

learning, system identification, domain adaptation, and image processing Tackles large-scale algorithms including conditional gradient methods;

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Regularization, Optimization, Kernels, and Support Vector Machines

, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing;

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Optimization for Machine Learning

An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both;

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Distributed Optimization Advances in Theories Methods and Applications

of distributed optimization in large-scale statistical learning, wireless sensor networks, and for optimal energy management in smart grids are discussed.;

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Distributed Optimization Advances in Theories Methods and Applications

of distributed optimization in large-scale statistical learning, wireless sensor networks, and for optimal energy management in smart grids are discussed.;

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Alternating Direction Method of Multipliers for Machine Learning

linearly constrained ones. Written by experts in machine learning and optimization, this is the first book providing a state-of-the-art review on;

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First-order and Stochastic Optimization Methods for Machine Learning

algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the;

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First-order and Stochastic Optimization Methods for Machine Learning

algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the;

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Machine Learning for Networking

learning for network slicing optimization, 5G system, user behavior prediction, multimedia, IoT, security and protection, optimization and new;

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Machine Learning for Networking: First International Conference, Mln 2018, Paris, France, November 27-29, 2018, Revised Selected Papers

for networks; Machine learning for network slicing optimization, 5G system, user behavior prediction, multimedia, IoT, security and protection;

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Cooperative and Graph Signal Processing

, optimization and control, machine learning, signal processing on graphs, signal processing for distributed communication, social networks from the;

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Intelligent Distributed Computing XIII

Learning, Multi-agent and Service-Based Distributed Systems, Distributed Algorithms and Optimization, Modeling Operational Processes, Social;

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Machine Learning for Networking

learning for network slicingoptimization, 5G system, user behavior prediction, multimedia, IoT, securityand protection, optimization and new;

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Distributed Optimization Game and Learning Algorithms

This book provides the fundamental theory of distributed optimization, game and learning. It includes those working directly;

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Distributed Optimization Game and Learning Algorithms

This book provides the fundamental theory of distributed optimization, game and learning. It includes those working directly;

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Federated Learning for Wireless Networks

, interference, and noise significantly affect the performance of FL. On the other hand, federated-reinforcement learning leverages distributed;

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Machine Learning

learning techniques as well as concepts and algorithmic versions for distributed optimization. Focusing on the physical reasoning behind;

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Communications and Networking

; Edge Computing and Distributed Machine Learning; Deep Learning.;

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Algorithms for Convex Optimization

In the last few years, Algorithms for Convex Optimization have revolutionized algorithm design, both for discrete and continuous;

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Algorithms for Convex Optimization

In the last few years, Algorithms for Convex Optimization have revolutionized algorithm design, both for discrete and continuous;

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A Tour of Data Science

, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data;

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A Tour of Data Science

, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data;

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Mathematical Theories of Machine Learning - Theory and Applications

This book studies mathematical theories of machine learning. The first part of the book explores the optimality and adaptivity of choosing;

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Advances in Distributed Computing and Machine Learning

This book presents recent advances in the field of distributed computing and machine learning, along with cutting-edge research in the;

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Machine Learning, Optimization, and Big Data

This book constitutes revised selected papers from the First International Workshop on Machine Learning, Optimization, and Big Data, MOD;

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