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Dynamic Fuzzy Machine Learning

Machine learning is widely used for data analysis. Dynamic fuzzy data are one of the most difficult types of data to analyse in the field;

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Soft Computing

Neuro-Fuzzy Inference System (ANFIS), Gaussian Process Regression (GPR), Relevance Vector Machine (RVM), Extreme Learning Machine (ELM). Chapter;

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14th International Conference on Theory and Application of Fuzzy Systems and Sof

include theory and application of soft computing, neuro-fuzzy technology, intelligent control, deep learning-machine learning, fuzzy logic in data;

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The International Conference on Advanced Machine Learning Technologies and Applications (Amlta2018)

cover current research in machine learning, big data, Internet of Things, biomedical engineering, fuzzy logic, security, and intelligence swarms;

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Genetic Algorithms, Fuzzy Systems & Website Classification

chain through fuzzy outranking techniques; fast web page classification without accessing the web page using machine learning techniques;

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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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Advanced Machine Learning Technologies and Applications

Group in Egypt (SRGE). The papers cover current research in machine learning, big data, Internet of Things, biomedical engineering, fuzzy logic;

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Applied Soft Computing

, hybrid models, machine learning, meta heuristics, neuro fuzzy system, and optimization principles. The book acts as a reference book for AI;

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Fuzzy Logic and Applications

submissions. In addition the book contains 3 keynote talks and 2 tutorials. The papers are organized in topical sections named: fuzzy machine learning;

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Advances in Machine Learning and Computational Intelligence

This book gathers selected high-quality papers presented at the International Conference on Machine Learning and Computational Intelligence;

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Handbook Of Machine Learning - Volume 1

This is a comprehensive book on the theories of artificial intelligence with an emphasis on their applications. It combines fuzzy logic and;

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Applied Machine Learning

. Coverage includes: *Supervised learning*Statistical learning*Learning with support vector machines (SVM)*Learning with neural networks (NN)*Fuzzy;

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Engineering Applications of Neural Networks

machine, machine learning applications, predictive models, fuzzy and recommender systems, recurrent neural networks, spiking neural networks.;

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Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification

an emphasis on fuzzy machine learning and deep learning algorithms. Both types of algorithms are described in such details that these can be;

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Intelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint

This book discusses various applications of machine learning using a new approach, the dynamic wavelet fingerprint technique, to identify;

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Intelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint

This book discusses various applications of machine learning using a new approach, the dynamic wavelet fingerprint technique, to identify;

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Multiple Fuzzy Classification Systems

preprocessed before classification. To achieve better performance than existing machine learning systems, fuzzy classifiers and rough sets can be;

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Deep Neuro-Fuzzy Systems with Python

different types of deep neuro fuzzy classifiers, fuzzy neurons, and the adaptive learning capability of the neural networks. The book concludes by;

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Advances in Machine Learning Applications in Software Engineering

of developing and maintaining larger and complex software systems in a dynamic and changing environment, machine learning methods have been playing an;

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Diffuse Algorithms for Neural and Neuro-Fuzzy Networks

Diffuse Algorithms for Neural and Neuro-Fuzzy Networks: With Applications in Control Engineering and Signal Processing presents new;

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Emerging Paradigms in Machine Learning

systems. Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account;

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Advanced Machine Learning Approaches in Cancer Prognosis

This book introduces a variety of advanced machine learning approaches covering the areas of neural networks, fuzzy logic, and hybrid;

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Multi-Agent Machine Learning

The book begins with a chapter on traditional methods of supervised learning, covering recursive least squares learning, mean square error;

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Congress on Intelligent Systems

learning, unsupervised learning, fuzzy logic, rough sets, computational optimization, and neuro-fuzzy systems.;

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Congress on Intelligent Systems

learning, unsupervised learning, fuzzy logic, rough sets, computational optimization, and neuro fuzzy systems.;

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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

This book constitutes the refereed conference proceedings of the 15th International Conference on Rough Sets, Fuzzy Sets, Data Mining and;

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Pattern Recognition and Machine Intelligence

selected from 90 submissions. They were organized in topical sections named: foundations of machine learning; image processing; image retrieval;

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