Statistical Learning Of Complex Data

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Statistical Learning Of Complex Data

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Author : Francesca Greselin
language : en
Publisher: Springer Nature
Release Date : 2019-09-06

Statistical Learning Of Complex Data written by Francesca Greselin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-06 with Mathematics categories.

This book of peer-reviewed contributions presents the latest findings in classification, statistical learning, data analysis and related areas, including supervised and unsupervised classification, clustering, statistical analysis of mixed-type data, big data analysis, statistical modeling, graphical models and social networks. It covers both methodological aspects as well as applications to a wide range of fields such as economics, architecture, medicine, data management, consumer behavior and the gender gap. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary. This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field of data analysis and classification. It gathers selected and peer-reviewed contributions presented at the 11th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2017), held in Milan, Italy, on September 13–15, 2017.

An Introduction To Statistical Learning

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Author : Gareth James
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-24

An Introduction To Statistical Learning written by Gareth James and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-24 with Mathematics categories.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

Classification Big Data Analysis And Statistical Learning

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Author : Francesco Mola
language : en
Publisher: Springer
Release Date : 2018-02-21

Classification Big Data Analysis And Statistical Learning written by Francesco Mola and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-21 with Mathematics categories.

This edited book focuses on the latest developments in classification, statistical learning, data analysis and related areas of data science, including statistical analysis of large datasets, big data analytics, time series clustering, integration of data from different sources, as well as social networks. It covers both methodological aspects as well as applications to a wide range of areas such as economics, marketing, education, social sciences, medicine, environmental sciences and the pharmaceutical industry. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary. This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field. The peer-reviewed contributions were presented at the 10th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, held in Santa Margherita di Pula (Cagliari), Italy, October 8–10, 2015.

Practical Statistical Learning And Data Science Methods

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Author : O. Olawale Awe
language : en
Publisher: Springer Nature
Release Date : 2024-12-27

Practical Statistical Learning And Data Science Methods written by O. Olawale Awe and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-27 with Computers categories.

This contributed volume offers practical implementation strategies for statistical learning and data science techniques, with fully peer-reviewed papers that embody insights and experiences gathered within the LISA 2020 Global Network. Through a series of compelling case studies, readers are immersed in practical methodologies, real-world applications, and innovative approaches in statistical learning and data science. Topics covered in this volume span a wide array of applications, including machine learning in health data analysis, deep learning models for precipitation modeling, interpretation techniques for machine learning models in BMI classification for obesity studies, as well as a comparative analysis of sampling methods in machine learning health applications. By addressing the evolving landscape of data analytics in many ways, this volume serves as a valuable resource for practitioners, researchers, and students alike. The LISA 2020 Global Network is dedicated to enhancing statistical and data science capabilities in developing countries through the establishment of collaboration laboratories, also known as “stat labs.” These stat labs function as engines for development, nurturing the next generation of collaborative statisticians and data scientists while providing essential research infrastructure for researchers, data producers, and decision-makers.

Statistical Learning For Complex Data To Enable Precision Medicine Strategies

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Author :
language : en
Publisher:
Release Date : 2023

Statistical Learning For Complex Data To Enable Precision Medicine Strategies written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.

The 2020 International Conference On Machine Learning And Big Data Analytics For Iot Security And Privacy

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Author : John MacIntyre
language : en
Publisher: Springer Nature
Release Date : 2020-11-03

The 2020 International Conference On Machine Learning And Big Data Analytics For Iot Security And Privacy written by John MacIntyre and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-03 with Computers categories.

This book presents the proceedings of The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2020), held in Shanghai, China, on November 6, 2020. Due to the COVID-19 outbreak problem, SPIoT-2020 conference was held online by Tencent Meeting. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including novel machine learning and big data analytics methods for IoT security, data mining and statistical modelling for the secure IoT and machine learning-based security detecting protocols, which inspire the development of IoT security and privacy technologies. The contributions cover a wide range of topics: analytics and machine learning applications to IoT security; data-based metrics and risk assessment approaches for IoT; data confidentiality and privacy in IoT; and authentication and access control for data usage in IoT. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and provides a useful reference guide for newcomers to the IoT security and privacy field.

Explainable Machine Learning For Geospatial Data Analysis

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Author : Courage Kamusoko
language : en
Publisher: CRC Press
Release Date : 2024-12-06

Explainable Machine Learning For Geospatial Data Analysis written by Courage Kamusoko and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-06 with Technology & Engineering categories.

Explainable machine learning (XML), a subfield of AI, is focused on making complex AI models understandable to humans. This book highlights and explains the details of machine learning models used in geospatial data analysis. It demonstrates the need for a data-centric, explainable machine learning approach to obtain new insights from geospatial data. It presents the opportunities, challenges, and gaps in the machine and deep learning approaches for geospatial data analysis and how they are applied to solve various environmental problems in land cover changes and in modeling forest canopy height and aboveground biomass density. The author also includes guidelines and code scripts (R, Python) valuable for practical readers. Features Data-centric explainable machine learning (ML) approaches for geospatial data analysis. The foundations and approaches to explainable ML and deep learning. Several case studies from urban land cover and forestry where existing explainable machine learning methods are applied. Descriptions of the opportunities, challenges, and gaps in data-centric explainable ML approaches for geospatial data analysis. Scripts in R and python to perform geospatial data analysis, available upon request. This book is an essential resource for graduate students, researchers, and academics working in and studying data science and machine learning, as well as geospatial data science professionals using GIS and remote sensing in environmental fields.

Statistical Learning And Data Science

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Author : Mireille Gettler Summa
language : en
Publisher: CRC Press
Release Date : 2011-12-19

Statistical Learning And Data Science written by Mireille Gettler Summa and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-12-19 with Business & Economics categories.

Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis, factor analysis, and low dimensionality mapping methods continually being updated, have reached new heights of achievement in the incredibly rich data wor

Cloud Data Science Harnessing Azure Machine Learning With Python

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Author : Peter Jones
language : en
Publisher: Walzone Press
Release Date : 2025-01-12

Cloud Data Science Harnessing Azure Machine Learning With Python written by Peter Jones and has been published by Walzone Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-12 with Computers categories.

Unlock the full potential of your data with "Cloud Data Science: Harnessing Azure Machine Learning with Python." This comprehensive guide equips you with the knowledge and skills to leverage the power of Azure Machine Learning and the versatility of Python to innovate and streamline your machine learning workflows. From setting up your Azure Machine Learning workspace to deploying sophisticated models, this book covers essential techniques and advanced methodologies in a clear, practical format. Dive into core topics such as data management, automated machine learning workflows, model optimization, and real-time monitoring to ensure your projects are scalable, efficient, and effective. Whether you're a data scientist, machine learning engineer, or a professional seeking to enhance your understanding of cloud-based machine learning, this book offers invaluable insights and hands-on examples to help you transform vast amounts of data into actionable insights. Explore real-world case studies across various industries, learn to overcome common challenges, and discover best practices for implementing machine learning projects successfully. "Cloud Data Science: Harnessing Azure Machine Learning with Python" is your gateway to mastering data science in the cloud and advancing your professional capabilities in the future of technology.

Machine Learning With Health Care Perspective

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Author : Vishal Jain
language : en
Publisher: Springer Nature
Release Date : 2020-03-09

Machine Learning With Health Care Perspective written by Vishal Jain and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-09 with Technology & Engineering categories.

This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Providing a unique compendium of current and emerging machine learning paradigms for healthcare informatics, it reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. Further, it describes techniques for applying machine learning within organizations and explains how to evaluate the efficacy, suitability, and efficiency of such applications. Featuring illustrative case studies, including how chronic disease is being redefined through patient-led data learning, the book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges.