359.866 hasil untuk machine learning
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Buku Teks lengkap
Machine Learning for Brain Disorders
This Open Access volume provides readers with an up-to-date and comprehensive guide to both methodological and applicative aspects of machine learning (ML) for brain disorders. The chapters in this book are organized into five parts. Part One presents the fundamentals of ML. Part Two looks at the…
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Machine Learning in Biomolecular Simulations
This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from…
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Gaussian Processes for Machine Learning
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received…
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Machine Learning for Data Streams
A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are…
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Convex Optimization for Machine Learning
…in a widening array of practical contexts with a particular emphasis on machine learning. The first part of the book covers core concepts of convex sets, convex functions, and related basic definitions that serve understanding convex optimization and its corresponding models. The second part deals…
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Machine Learning Applications in Seismology
The purpose of this Special Issue is to immerse readers in the transformative impact of machine learning-based artificial intelligence on seismology. From digital seismic data processing to the development of structured seismic catalogs, AI technology is poised to revolutionize our understanding of…
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Entropy in Machine Learning Applications
The aim of this reprint is to inform readers of the latest developments in methods and applications of machine learning and deep learning in certain fields, including the following: a semantically enhanced social network user alignment algorithm to perform user alignment; a congestion control…
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Computational Intelligence and Machine Learning
This Special Issue presents a curated collection of ten research articles that reflect current trends and challenges in computational intelligence and machine learning. The selected works cover a broad spectrum of methods and applications, including personalized recommendation systems, social-aware…
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Applied Mathematics and Machine Learning
The simultaneous availability of large datasets and high-performance computing capability in recent years has enabled the rapid development of powerful machine learning algorithms. On the one hand, state-of-the-art machine learning techniques have transformed many areas of science and engineering…
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Information Theory and Machine Learning
The recent successes of machine learning, especially regarding systems based on deep neural networks, have encouraged further research activities and raised a new set of challenges in understanding and designing complex machine learning algorithms. New applications require learning algorithms to be…
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Machine Learning for Biomedical Application
…those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data…
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27: Machine learning and law
This chapter will provide an overview of the applications of machine learning in the legal domain, both theoretical possibilities and applications already in use. As an understanding of IT concepts is crucial for a comprehensive discussion of their applications, the chapter opens with an informal…
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Computational Optimizations for Machine Learning
The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and…
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New Advances in Machine Learning
The purpose of this book is to provide an up-to-date and systematical introduction to the principles and algorithms of machine learning. The definition of learning is broad enough to include most tasks that we commonly call “learning” tasks, as we use the word in daily life. It is also broad enough…
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Social Media and Machine Learning
Social media has transformed society and the way people interact with each other. The volume and speed in which new content is being generated surpasses the processing capacity of machine learning systems. Analyzing such data demands new approaches coming from natural language processing, text…
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Machine Learning for Energy Systems
This volume deals with recent advances in and applications of computational intelligence and advanced machine learning methods in power systems, heating and cooling systems, and gas transportation systems. The optimal coordinated dispatch of the multi-energy microgrids with renewable generation and…
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Mathematical Optimization for Machine Learning
Mathematical optimization and machine learning are closely related. This proceedings volume of the Thematic Einstein Semester 2023 of the Berlin Mathematics Research Center MATH+ collects recent progress on their interplay in topics such as discrete optimization, nonlinear programming, optimal…
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An integrated framework for applying machine learning in intelligent decision support systems
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Artikel Jurnal Teks lengkap
Physically-constrained evapotranspiration models with machine learning parameterization outperform pure machine learning: Critical role of domain knowledge
Physics-informed machine learning techniques have emerged to tackle challenges inherent in pure machine learning (ML) approaches. One such technique, the hybrid approach, has been introduced to estimate terrestrial evapotranspiration (ET), a crucial variable linking water, energy, and carbon…
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