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Feature Learning and Understanding: Algorithms and Applications

This book covers the essential concepts and strategies within traditional and cutting-edge feature learning methods thru both theoretical;

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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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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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Feature Engineering for Machine Learning and Data Analytics

Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little;

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Feature Engineering for Machine Learning and Data Analytics

Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little;

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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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Unsupervised Learning Algorithms

of unlabeled data, unsupervised learning algorithms, which can automatically discover interesting and useful patterns in such data, have gained;

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Spectral Feature Selection for Data Mining

existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications. This technique represents a;

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Spectral Feature Selection for Data Mining

existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications. This technique represents a;

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Hybrid Methods in Feature Selection

enormity may cause serious problems to many machine learning algorithms with respect to scalability and learning performance. Therefore, feature;

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Advances in Swarm Intelligence, Part I

The two-volume set (LNCS 6728 and 6729) constitutes the refereed proceedings of the International Conference on Swarm Intelligence, ICSI;

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An Introduction to Machine Learning

This textbook offers a comprehensive introduction to Machine Learning techniques and algorithms. This Third Edition covers newer approaches;

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Artificial Intelligence with Python

the cloud, recurrent neural networks, deep learning models, and feature selection and engineering Learn more about deep learning algorithms;

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Feature Learning and Understanding

This book covers the essential concepts and strategies within traditional and cutting-edge feature learning methods thru both theoretical;

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Condition Monitoring with Vibration Signals

presents new methods including machine learning and compressive sampling, which help to improve safety, reliability, and performance.A;

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Machine Learning Algorithms

Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This;

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Machine Learning with PySpark

Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business;

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Biosignal Processing and Classification Using Computational Learning and Intelligence

Biosignal Processing and Classification Using Computational Learning and Intelligence: Principles, Algorithms and Applications posits an;

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Multimodal Scene Understanding

Multimodal Scene Understanding: Algorithms, Applications and Deep Learning presents recent advances in multi-modal computing, with a;

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Solving Large Scale Learning Tasks. Challenges and Algorithms

theoreticians and practitioners on data preprocessing, modeling, learning, and evaluation. Topics include data-mining and machine-learning algorithms;

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Hands-On Genetic Algorithms with Python

Explore the ever-growing world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning;

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Machine Learning Algorithms

Study patterns and make predictions across various datasets Book DescriptionMachine learning has gained tremendous popularity for its powerful;

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Machine Learning for Biometrics

Machine Learning for Biometrics: Concepts, Algorithms and Applications highlights the fundamental concepts of machine learning;

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Supervised and Unsupervised Learning for Data Science

in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded;

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Introduction to Deep Learning Business Applications for Developers

Discover the potential applications, challenges, and opportunities of deep learning from a business perspective with technical examples;

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