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Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition

reference text for graduate courses on swarm optimization, data clustering and classification, content-based multimedia search, and biomedical;

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Fractional Order Darwinian Particle Swarm Optimization

offers a valuable resource for researchers in the fields of robotics, sports science, pattern recognition and machine learning, as well as for;

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Advances in Swarm Intelligence, Part I

methods, machine learning methods, feature selection algorithms, pattern recognition methods, intelligent control, other optimization algorithms;

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Advances in Swarm Intelligence

optimization; neural networks; machine learning; identification and recognition; social computing and knowledge graph; service quality and energy;

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Advances in Swarm Intelligence

optimization; neural networks; machine learning; identification and recognition; social computing and knowledge graph; service quality and energy;

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Advances in Swarm Intelligence

177 submissions. They cover topics such as: Swarm Intelligence and Nature-Inspired Computing; Swarm-based Computing Algorithms for;

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Advances in Swarm Intelligence

computing; swarm-based computing algorithms for optimization; particle swarm optimization; ant colony optimization; brain storm optimization;

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Intelligent Computing Theories and Application

learning and data analysis for medical and engineering applications; evolutionary computation and learning; independent component analysis;

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Intelligent Computing Theories and Application

learning and data analysis for medical and engineering applications; evolutionary computation and learning; independent component analysis;

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Intelligent Computing Methodologies

learning and data analysis for medical and engineering applications; evolutionary computation and learning; independent component analysis;

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Hybrid Metaheuristics for Image Analysis

techniques such as genetic algorithms and particle swarm optimization. The contributions provide a multidimensional approach, and the book;

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Intelligent Computing Theories and Application

in topical sections such as Evolutionary Computation and Learning; Neural Networks; Nature Inspired Computing and Optimization; Signal Processing;

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Advances in Swarm Intelligence

of swarm intelligence research; novel swarm-based optimization algorithms; swarming behaviour; some swarm intelligence algorithms and their;

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Advances in Swarm Intelligence

of swarm intelligence research; novel swarm-based optimization algorithms; swarming behaviour; some swarm intelligence algorithms and their;

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Advances in Swarm and Computational Intelligence

-based optimization algorithms and applications; particle swarm opt8imization; ant colony optimization; artificial bee colony algorithms;

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Advances in Swarm and Computational Intelligence

-based optimization algorithms and applications; particle swarm opt8imization; ant colony optimization; artificial bee colony algorithms;

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Advances in Swarm and Computational Intelligence

-based optimization algorithms and applications; particle swarm opt8imization; ant colony optimization; artificial bee colony algorithms;

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Pattern Recognition and Computational Intelligence Techniques Using Matlab

This book presents the complex topic of using computational intelligence for pattern recognition in a straightforward and applicable way;

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Swarm Intelligence Based Optimization

learning, data mining, data clustering, decision making and multi-agent systems based on swarm intelligence principles; adaptation and applications;

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Intelligent Computing Theories and Technology

; swarm intelligence and optimization; unsupervised and reinforcement learning; intelligent computing in bioinformatics; intelligent computing;

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Classification Analysis of DNA Microarrays

involving particle swarm optimization and ant colony optimization.;

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Congress on Intelligent Systems

classification, soft computing for machine vision, pattern recognition, human-computer interaction, robotic devices and systems, autonomous vehicles;

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Congress on Intelligent Systems

classification, soft computing for machine vision, pattern recognition, human computer interaction, robotic devices and systems, autonomous vehicles;

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Intelligent Computing Theories and Methodologies

optimization; pattern recognition and signal processing; image processing; biomedical informatics theory and methods; differential evolution, particle;

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Intelligent Computing Theories and Methodologies

optimization; pattern recognition and signal processing; image processing; biomedical informatics theory and methods; differential evolution, particle;

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Proceedings of International Conference on Scientific and Natural Computing

computing, genetic programming, particle swarm optimization, neuro computing, hybrid methods, deep learning, including convolutional neural networks;

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Evolutionary Machine Learning Techniques

methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning;

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