Advances In Machine Learning And Data Analysis

Advances in Machine Learning and Data Analysis
Publisher Springer Science & Business Media
Release Date
Category Computers
Total Pages 239
ISBN 9048131774
Rating 4/5 from 21 reviews
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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 conference. Topics covered include Expert system, Intelligent decision making, Knowledge-based systems, Knowledge extraction, Data analysis tools, Computational biology, Optimization algorithms, Experiment designs, Complex system identification, Computational modeling, and industrial applications. Advances in Machine Learning and Data Analysis offers the state of the art of tremendous advances in machine learning and data analysis and also serves as an excellent reference text for researchers and graduate students, working on machine learning and data analysis.

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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 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 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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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 Financial Machine Learning
  • Author : Marcos Lopez de Prado
  • Publisher : John Wiley & Sons
  • Release Date : 2018-02-21

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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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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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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Advances in Statistical Models for Data Analysis
  • Author : Isabella Morlini,Tommaso Minerva,Maurizio Vichi
  • Publisher : Springer
  • Release Date : 2015-09-04

This edited volume focuses on recent research results in classification, multivariate statistics and machine learning and highlights advances in statistical models for data analysis. The volume provides both methodological developments and contributions to a wide range of application areas such as economics, marketing, education, social sciences and environment. The papers

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Advances in Machine Learning and Data Analysis
  • Author : Mahyar Amouzegar
  • Publisher : Springer
  • Release Date : 2010-04-29

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 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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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 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 Data Analysis with Computational Intelligence Methods
  • Author : Adam E Gawęda,Janusz Kacprzyk,Leszek Rutkowski,Gary G. Yen
  • Publisher : Springer
  • Release Date : 2017-09-21

This book is a tribute to Professor Jacek Żurada, who is best known for his contributions to computational intelligence and knowledge-based neurocomputing. It is dedicated to Professor Jacek Żurada, Full Professor at the Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, J.B. Speed School of Engineering, University of

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Machine Learning for Big Data Analysis
  • Author : Siddhartha Bhattacharyya,Hrishikesh Bhaumik,Anirban Mukherjee,Sourav De
  • Publisher : Walter de Gruyter GmbH & Co KG
  • Release Date : 2018-12-17

This volume comprises six well-versed contributed chapters devoted to report the latest fi ndings on the applications of machine learning for big data analytics. Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them.

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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, the material discussed in

GET BOOK