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Download the fantastic book titled Machine Learning Paradigms written by Maria Virvou, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "Machine Learning Paradigms", which was released on 16 March 2019. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Technology & Engineering genre.

Summary of Machine Learning Paradigms by Maria Virvou PDF

This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators’ and learners’ data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including: • Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation; • Using learning analytics to predict student performance; • Using learning analytics to create learning materials and educational courses; and • Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning. The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest.

Machine Learning Paradigms

Detail About Machine Learning Paradigms PDF

  • Author : Maria Virvou
  • Publisher : Springer
  • Genre : Technology & Engineering
  • Total Pages : 223 pages
  • ISBN : 3030137430
  • PDF File Size : 50,5 Mb
  • Language : English
  • Rating : 4/5 from 21 reviews

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Machine Learning Paradigms

Machine Learning Paradigms
  • Publisher : Springer
  • File Size : 29,9 Mb
  • Release Date : 16 March 2019
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This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators’

Machine Learning Paradigms

Machine Learning Paradigms
  • Publisher : Springer Nature
  • File Size : 35,7 Mb
  • Release Date : 23 July 2020
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At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and

Machine Learning Paradigms

Machine Learning Paradigms
  • Publisher : Springer
  • File Size : 40,8 Mb
  • Release Date : 06 July 2019
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This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at providing, in hard-copy and soft-copy form, books on

Machine Learning Paradigms

Machine Learning Paradigms
  • Publisher : Springer
  • File Size : 28,6 Mb
  • Release Date : 03 July 2018
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This book explores some of the emerging scientific and technological areas in which the need for data analytics arises and is likely to play a significant role in the years

Fusion of Machine Learning Paradigms

Fusion of Machine Learning Paradigms
  • Publisher : Springer Nature
  • File Size : 46,5 Mb
  • Release Date : 06 February 2023
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This book aims at updating the relevant computer science-related research communities, including professors, researchers, scientists, engineers and students, as well as the general reader from other disciplines, on the most

Machine Learning Paradigms: Theory and Application

Machine Learning Paradigms: Theory and Application
  • Publisher : Springer
  • File Size : 42,6 Mb
  • Release Date : 08 December 2018
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The book focuses on machine learning. Divided into three parts, the first part discusses the feature selection problem. The second part then describes the application of machine learning in the

Algorithms in Machine Learning Paradigms

Algorithms in Machine Learning Paradigms
  • Publisher : Springer Nature
  • File Size : 39,7 Mb
  • Release Date : 03 January 2020
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This book presents studies involving algorithms in the machine learning paradigms. It discusses a variety of learning problems with diverse applications, including prediction, concept learning, explanation-based learning, case-based (exemplar-based) learning,

Sequence Learning

Sequence Learning
  • Publisher : Springer
  • File Size : 37,9 Mb
  • Release Date : 29 June 2003
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Sequential behavior is essential to intelligence in general and a fundamental part of human activities, ranging from reasoning to language, and from everyday skills to complex problem solving. Sequence learning

Emerging Paradigms in Machine Learning

Emerging Paradigms in Machine Learning
  • Publisher : Springer Science & Business Media
  • File Size : 31,5 Mb
  • Release Date : 31 July 2012
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This book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The multidisciplinary nature of machine