Advances In Machine Learning And Data Mining For Astronomy

Advances in Machine Learning and Data Mining for Astronomy
Publisher CRC Press
Release Date
Category Computers
Total Pages 744
ISBN 9781439841730
Rating 4/5 from 21 reviews
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Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book’s introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

Advances in Machine Learning and Data Mining for Astronomy
  • Author : Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava
  • Publisher : CRC Press
  • Release Date : 2012-03-29

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in

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Advances in Financial Machine Learning
  • Author : Marcos Lopez de Prado
  • Publisher : John Wiley & Sons
  • Release Date : 2018-01-23

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will

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Advances in Machine Learning and Data Science
  • Author : Damodar Reddy Edla,Pawan Lingras,Venkatanareshbabu K.
  • Publisher : Springer
  • Release Date : 2018-05-16

The Volume of “Advances in Machine Learning and Data Science - Recent Achievements and Research Directives” constitutes the proceedings of First International Conference on Latest Advances in Machine Learning and Data Science (LAMDA 2017). The 37 regular papers presented in this volume were carefully reviewed and selected from 123 submissions. These days we

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Advances in Data Science
  • Author : Edwin Diday,Rong Guan,Gilbert Saporta,Huiwen Wang
  • Publisher : John Wiley & Sons
  • Release Date : 2020-02-05

Data science unifies statistics, data analysis and machine learning to achieve a better understanding of the masses of data which are produced today, and to improve prediction. Special kinds of data (symbolic, network, complex, compositional) are increasingly frequent in data science. These data require specific methodologies, but there is a

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Advances in Deep Learning
  • Author : M. Arif Wani,Farooq Ahmad Bhat,Saduf Afzal,Asif Iqbal Khan
  • Publisher : Springer
  • Release Date : 2019-03-14

This book introduces readers to both basic and advanced concepts in deep network models. It covers state-of-the-art deep architectures that many researchers are currently using to overcome the limitations of the traditional artificial neural networks. Various deep architecture models and their components are discussed in detail, and subsequently illustrated by

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Machine Learning Paradigms
  • Author : George A. Tsihrintzis,Lakhmi C. Jain
  • Publisher : Springer Nature
  • Release Date : 2020-08-24

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 towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some

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Machine Learning Paradigms
  • Author : Maria Virvou,Efthimios Alepis,George A. Tsihrintzis,Lakhmi C. Jain
  • Publisher : Springer
  • Release Date : 2019-03-16

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

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Advances in Machine Learning and Data Analysis
  • Author : Mahyar Amouzegar
  • Publisher : Springer Science & Business Media
  • Release Date : 2009-10-27

A large international conference on Advances in Machine Learning and Data Analysis was held in UC Berkeley, California, USA, October 22-24, 2008, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2008). This volume contains sixteen revised and extended research articles written by prominent researchers participating in the

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Advances in Machine Learning Applications in Software Engineering
  • Author : Zhang, Du,Tsai, Jeffery J.P.
  • Publisher : IGI Global
  • Release Date : 2006-10-31

"This book provides analysis, characterization and refinement of software engineering data in terms of machine learning methods. It depicts applications of several machine learning approaches in software systems development and deployment, and the use of machine learning methods to establish predictive models for software quality while offering readers suggestions by

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Advances in Machine Learning and Computational Intelligence
  • Author : Srikanta Patnaik,Xin-She Yang,Ishwar K. Sethi
  • Publisher : Springer Nature
  • Release Date : 2020-07-25

This book gathers selected high-quality papers presented at the International Conference on Machine Learning and Computational Intelligence (ICMLCI-2019), jointly organized by Kunming University of Science and Technology and the Interscience Research Network, Bhubaneswar, India, from April 6 to 7, 2019. Addressing virtually all aspects of intelligent systems, soft computing and machine learning, the

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The Era of Artificial Intelligence  Machine Learning  and Data Science in the Pharmaceutical Industry
  • Author : Stephanie K. Ashenden
  • Publisher : Academic Press
  • Release Date : 2021-05-07

The Era of Artificial Intelligence, Machine Learning and Data Science in the Pharmaceutical Industry examines the drug discovery process, assessing how new technologies have improved effectiveness. Artificial intelligence and machine learning are considered the future for a wide range of disciplines and industries, including the pharmaceutical industry. In an environment

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Deep Learning for Data Analytics
  • Author : Himansu Das,Chittaranjan Pradhan,Nilanjan Dey
  • Publisher : Academic Press
  • Release Date : 2020-05-29

Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design

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Advances in Machine Learning and Data Mining for Astronomy
  • Author : Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava
  • Publisher : CRC Press
  • Release Date : 2012-03-29

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines

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AI 2018  Advances in Artificial Intelligence
  • Author : Tanja Mitrovic,Bing Xue,Xiaodong Li
  • Publisher : Springer
  • Release Date : 2018-12-03

This book constitutes the proceedings of the 31st Australasian Joint Conference on Artificial Intelligence, AI 2018, held in Wellington, New Zealand, in December 2018. The 50 full and 26 short papers presented in this volume were carefully reviewed and selected from 125 submissions. The paper were organized in topical sections named: agents, games and robotics;

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Machine Learning and Data Science Blueprints for Finance
  • Author : Hariom Tatsat,Sahil Puri,Brad Lookabaugh
  • Publisher : "O'Reilly Media, Inc."
  • Release Date : 2020-10-01

Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement

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