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Machine Learning and Metaheuristics Algorithms, and Applications

This book constitutes the refereed proceedings of the First Symposium on Machine Learning and Metaheuristics Algorithms, and Applications;

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Machine Learning and Metaheuristics Algorithms and Applications

This book constitutes the refereed proceedings of the Second Symposium on Machine Learning and Metaheuristics Algorithms, and Applications;

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Metaheuristics in Machine Learning

This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered;

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Metaheuristics

and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations;

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Data-Driven Evolutionary Optimization

Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning;

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Machine Intelligence and Soft Computing

computation, machine learning, metaheuristics and swarm intelligence, neuro-fuzzy system, soft computing and decision support systems, soft computing;

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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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Machine Learning Theoretical Foundations and Practical Applications

in conjunction with 16th edition of ICDCIT. The book covers topics, like machine learning and its applications, statistical learning, neural network;

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Machine Learning with SAP

Work smarter with machine learning! Begin with core machine learning concepts-types of learning, algorithms, data preparation, and more;

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Metaheuristics

The book presents eight well-known and often used algorithms besides nine newly developed algorithms by the first author and his students;

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Parallel Metaheuristics

of experts in parallelism and metaheuristics to provide a much-needed synthesis of these two fields. Readers discover how metaheuristic techniques;

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Machine Learning and Big Data

learning, including those that are solving technology requirements, evaluation of methodology advances and algorithm demonstrations. The intent;

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Automated Design of Machine Learning and Search Algorithms

This book presents recent advances in automated machine learning (AutoML) and automated algorithm design and indicates the future;

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Machine Learning for OpenCV 4

A practical guide to understanding the core machine learning and deep learning algorithms, and implementing them to create intelligent;

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Machine Learning The New AI

systems, face recognition, and driverless cars. Today, machine learning underlies a range of applications we use every day, from product;

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Machine Learning in Healthcare

Machine Learning in Healthcare discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. It;

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Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021)

sections, covering the following topics: Deep Learning and Applications; Smart Grid, Internet of Things, and Mobil Applications; Machine Learning;

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Metaheuristics for Structural Design and Analysis

Metaheuristics for Structural Design and Analysis discusses general properties and types of metaheuristic techniques, basic principles;

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Nature-Inspired Computation in Data Mining and Machine Learning

mining and machine learning. Data mining, machine learning and nature-inspired computation are current hot research topics due to their;

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Nature-Inspired Computation in Data Mining and Machine Learning

mining and machine learning. Data mining, machine learning and nature-inspired computation are current hot research topics due to their;

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Recent Developments in Metaheuristics

studies. The book is divided into two sections. Part I is focused on new optimization and modeling techniques based on metaheuristics. The;

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Algorithms in Machine Learning Paradigms

, feature extraction-based learning, optimization-based learning, quantum-inspired learning, multi-criteria-based learning and hybrid intelligence;

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Algorithms in Machine Learning Paradigms

, feature extraction-based learning, optimization-based learning, quantum-inspired learning, multi-criteria-based learning and hybrid intelligence;

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Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent

With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement;

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Human and Machine Learning

With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement;

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Brain and Nature-Inspired Learning, Computation and Recognition

Brain and Nature-Inspired Learning, Computation and Recognition presents a systematic analysis of neural networks, natural computing;

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Handbook of Research on Applications and Implementations of Machine Learning Techniques

it's detecting disease or generating algorithms, deep learning techniques are advancing exponentially. Researchers and professionals need a;

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