Multiblock Data Fusion In Statistics And Machine Learning

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Multiblock Data Fusion in Statistics and Machine Learning

Author : Age K. Smilde
Publisher : John Wiley & Sons
ISBN 13 : 1119600995
Total Pages : 354 pages
Book Rating : 4.1/5 (196 download)

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Book Synopsis Multiblock Data Fusion in Statistics and Machine Learning by : Age K. Smilde

Download or read book Multiblock Data Fusion in Statistics and Machine Learning written by Age K. Smilde and published by John Wiley & Sons. This book was released on 2022-05-03 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiblock Data Fusion in Statistics and Machine Learning Explore the advantages and shortcomings of various forms of multiblock analysis, and the relationships between them, with this expert guide Arising out of fusion problems that exist in a variety of fields in the natural and life sciences, the methods available to fuse multiple data sets have expanded dramatically in recent years. Older methods, rooted in psychometrics and chemometrics, also exist. Multiblock Data Fusion in Statistics and Machine Learning: Applications in the Natural and Life Sciences is a detailed overview of all relevant multiblock data analysis methods for fusing multiple data sets. It focuses on methods based on components and latent variables, including both well-known and lesser-known methods with potential applications in different types of problems. Many of the included methods are illustrated by practical examples and are accompanied by a freely available R-package. The distinguished authors have created an accessible and useful guide to help readers fuse data, develop new data fusion models, discover how the involved algorithms and models work, and understand the advantages and shortcomings of various approaches. This book includes: A thorough introduction to the different options available for the fusion of multiple data sets, including methods originating in psychometrics and chemometrics Practical discussions of well-known and lesser-known methods with applications in a wide variety of data problems Included, functional R-code for the application of many of the discussed methods Perfect for graduate students studying data analysis in the context of the natural and life sciences, including bioinformatics, sensometrics, and chemometrics, Multiblock Data Fusion in Statistics and Machine Learning: Applications in the Natural and Life Sciences is also an indispensable resource for developers and users of the results of multiblock methods.

Advances in methods and tools for multi-omics data analysis

Author : Ornella Cominetti
Publisher : Frontiers Media SA
ISBN 13 : 2832523420
Total Pages : 184 pages
Book Rating : 4.8/5 (325 download)

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Book Synopsis Advances in methods and tools for multi-omics data analysis by : Ornella Cominetti

Download or read book Advances in methods and tools for multi-omics data analysis written by Ornella Cominetti and published by Frontiers Media SA. This book was released on 2023-05-12 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Fundamentals and Applications of Multiway Data Analysis

Author : Alejandro Olivieri
Publisher : Elsevier
ISBN 13 : 0443132623
Total Pages : 710 pages
Book Rating : 4.4/5 (431 download)

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Book Synopsis Fundamentals and Applications of Multiway Data Analysis by : Alejandro Olivieri

Download or read book Fundamentals and Applications of Multiway Data Analysis written by Alejandro Olivieri and published by Elsevier. This book was released on 2024-01-19 with total page 710 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fundamentals and Applications of Multiway Data Analysis provides comprehensive coverage of the main aspects of multiway analysis, including selected applications that can resolve complex analytical chemistry problems. This book follows on from Fundamentals and Analytical Applications of Multiway Calibration, (2015) by addressing new theoretical analysis and applications on subjects beyond multiway calibration and devoted to the analysis of multiway data for other purposes. Specifically, this new volume presents researchers a set of effective tools they can use to obtain the maximum information from instrumental data. This book includes the most advanced techniques, methods and algorithms related to multiway modelling for solving calibration and classification tasks, and the way they can be applied. This book collects contributions from a selected number of well-known and active chemometric research groups across the world, each covering one or more subjects where their expertise is recognized and appreciated. Includes chapters written by renowned international authors, all currently active in the subject field Presents coverage of all the main aspects of multi-way analytical data analysis, concerning the two main areas of interest: data generation and algorithmic models for data processing Provides up-to-date material with reference to current literature on the subject

Kernel-based Data Fusion for Machine Learning

Author : Shi Yu
Publisher : Springer
ISBN 13 : 3642194060
Total Pages : 223 pages
Book Rating : 4.6/5 (421 download)

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Book Synopsis Kernel-based Data Fusion for Machine Learning by : Shi Yu

Download or read book Kernel-based Data Fusion for Machine Learning written by Shi Yu and published by Springer. This book was released on 2011-03-29 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data fusion problems arise frequently in many different fields. This book provides a specific introduction to data fusion problems using support vector machines. In the first part, this book begins with a brief survey of additive models and Rayleigh quotient objectives in machine learning, and then introduces kernel fusion as the additive expansion of support vector machines in the dual problem. The second part presents several novel kernel fusion algorithms and some real applications in supervised and unsupervised learning. The last part of the book substantiates the value of the proposed theories and algorithms in MerKator, an open software to identify disease relevant genes based on the integration of heterogeneous genomic data sources in multiple species. The topics presented in this book are meant for researchers or students who use support vector machines. Several topics addressed in the book may also be interesting to computational biologists who want to tackle data fusion challenges in real applications. The background required of the reader is a good knowledge of data mining, machine learning and linear algebra.

Data Fusion and Data Mining for Power System Monitoring

Author : Arturo Román Messina
Publisher : CRC Press
ISBN 13 : 1000065936
Total Pages : 170 pages
Book Rating : 4.0/5 ( download)

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Book Synopsis Data Fusion and Data Mining for Power System Monitoring by : Arturo Román Messina

Download or read book Data Fusion and Data Mining for Power System Monitoring written by Arturo Román Messina and published by CRC Press. This book was released on 2020-06-03 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Fusion and Data Mining for Power System Monitoring provides a comprehensive treatment of advanced data fusion and data mining techniques for power system monitoring with focus on use of synchronized phasor networks. Relevant statistical data mining techniques are given, and efficient methods to cluster and visualize data collected from multiple sensors are discussed. Both linear and nonlinear data-driven mining and fusion techniques are reviewed, with emphasis on the analysis and visualization of massive distributed data sets. Challenges involved in realistic monitoring, visualization, and analysis of observation data from actual events are also emphasized, supported by examples of relevant applications. Features Focuses on systematic illustration of data mining and fusion in power systems Covers issues of standards used in the power industry for data mining and data analytics Applications to a wide range of power networks are provided including distribution and transmission networks Provides holistic approach to the problem of data mining and data fusion using cutting-edge methodologies and technologies Includes applications to massive spatiotemporal data from simulations and actual events

High-Level Data Fusion

Author : Subrata Das
Publisher : Artech House
ISBN 13 : 1596932821
Total Pages : 393 pages
Book Rating : 4.5/5 (969 download)

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Book Synopsis High-Level Data Fusion by : Subrata Das

Download or read book High-Level Data Fusion written by Subrata Das and published by Artech House. This book was released on 2008-01-01 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book explores object and situation fusion processes with an appropriate handling of uncertainties, and applies cutting-edge artificial intelligence and emerging technologies like particle filtering, spatiotemporal clustering, net-centricity, agent formalism, and distributed fusion together with essential Level 1 techniques and Level 1/2 interactions.

Linking and Mining Heterogeneous and Multi-view Data

Author : Deepak P
Publisher : Springer
ISBN 13 : 9783030018719
Total Pages : 343 pages
Book Rating : 4.0/5 (187 download)

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Book Synopsis Linking and Mining Heterogeneous and Multi-view Data by : Deepak P

Download or read book Linking and Mining Heterogeneous and Multi-view Data written by Deepak P and published by Springer. This book was released on 2019-02-04 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights research in linking and mining data from across varied data sources. The authors focus on recent advances in this burgeoning field of multi-source data fusion, with an emphasis on exploratory and unsupervised data analysis, an area of increasing significance with the pace of growth of data vastly outpacing any chance of labeling them manually. The book looks at the underlying algorithms and technologies that facilitate the area within big data analytics, it covers their applications across domains such as smarter transportation, social media, fake news detection and enterprise search among others. This book enables readers to understand a spectrum of advances in this emerging area, and it will hopefully empower them to leverage and develop methods in multi-source data fusion and analytics with applications to a variety of scenarios. Includes advances on unsupervised, semi-supervised and supervised approaches to heterogeneous data linkage and fusion; Covers use cases of analytics over multi-view and heterogeneous data from across a variety of domains such as fake news, smarter transportation and social media, among others; Provides a high-level overview of advances in this emerging field and empowers the reader to explore novel applications and methodologies that would enrich the field.

The Big R-Book

Author : Philippe J. S. De Brouwer
Publisher : John Wiley & Sons
ISBN 13 : 1119632765
Total Pages : 928 pages
Book Rating : 4.1/5 (196 download)

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Book Synopsis The Big R-Book by : Philippe J. S. De Brouwer

Download or read book The Big R-Book written by Philippe J. S. De Brouwer and published by John Wiley & Sons. This book was released on 2020-09-29 with total page 928 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces professionals and scientists to statistics and machine learning using the programming language R Written by and for practitioners, this book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use. It covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science/math background that is looking to quickly learn several practical technologies to enter or transition to the growing field of data science. The Big R-Book for Professionals: From Data Science to Learning Machines and Reporting with R includes nine parts, starting with an introduction to the subject and followed by an overview of R and elements of statistics. The third part revolves around data, while the fourth focuses on data wrangling. Part 5 teaches readers about exploring data. In Part 6 we learn to build models, Part 7 introduces the reader to the reality in companies, Part 8 covers reports and interactive applications and finally Part 9 introduces the reader to big data and performance computing. It also includes some helpful appendices. Provides a practical guide for non-experts with a focus on business users Contains a unique combination of topics including an introduction to R, machine learning, mathematical models, data wrangling, and reporting Uses a practical tone and integrates multiple topics in a coherent framework Demystifies the hype around machine learning and AI by enabling readers to understand the provided models and program them in R Shows readers how to visualize results in static and interactive reports Supplementary materials includes PDF slides based on the book’s content, as well as all the extracted R-code and is available to everyone on a Wiley Book Companion Site The Big R-Book is an excellent guide for science technology, engineering, or mathematics students who wish to make a successful transition from the academic world to the professional. It will also appeal to all young data scientists, quantitative analysts, and analytics professionals, as well as those who make mathematical models.

Information Fusion in Data Mining

Author : Prof. Vicenç Torra
Publisher : Springer
ISBN 13 : 3540365192
Total Pages : 232 pages
Book Rating : 4.5/5 (43 download)

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Book Synopsis Information Fusion in Data Mining by : Prof. Vicenç Torra

Download or read book Information Fusion in Data Mining written by Prof. Vicenç Torra and published by Springer. This book was released on 2013-06-05 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information fusion is becoming a major requirement in data mining and knowledge discovery in databases. This book presents some recent fusion techniques that are currently in use in data mining, as well as data mining applications that use information fusion. Special focus of the book is on information fusion in preprocessing, model building and information extraction with various applications.

Principles and Theory for Data Mining and Machine Learning

Author : Bertrand Clarke
Publisher : Springer Science & Business Media
ISBN 13 : 0387981357
Total Pages : 786 pages
Book Rating : 4.3/5 (879 download)

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Book Synopsis Principles and Theory for Data Mining and Machine Learning by : Bertrand Clarke

Download or read book Principles and Theory for Data Mining and Machine Learning written by Bertrand Clarke and published by Springer Science & Business Media. This book was released on 2009-07-21 with total page 786 pages. Available in PDF, EPUB and Kindle. Book excerpt: Extensive treatment of the most up-to-date topics Provides the theory and concepts behind popular and emerging methods Range of topics drawn from Statistics, Computer Science, and Electrical Engineering

Frontiers in Data Science

Author : Matthias Dehmer
Publisher : CRC Press
ISBN 13 : 1498799337
Total Pages : 395 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Frontiers in Data Science by : Matthias Dehmer

Download or read book Frontiers in Data Science written by Matthias Dehmer and published by CRC Press. This book was released on 2017-10-16 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: Frontiers in Data Science deals with philosophical and practical results in Data Science. A broad definition of Data Science describes the process of analyzing data to transform data into insights. This also involves asking philosophical, legal and social questions in the context of data generation and analysis. In fact, Big Data also belongs to this universe as it comprises data gathering, data fusion and analysis when it comes to manage big data sets. A major goal of this book is to understand data science as a new scientific discipline rather than the practical aspects of data analysis alone.

Data Science Fusion

Author : Nibedita Sahu
Publisher : Nibedita Sahu
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.2/5 (236 download)

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Book Synopsis Data Science Fusion by : Nibedita Sahu

Download or read book Data Science Fusion written by Nibedita Sahu and published by Nibedita Sahu. This book was released on 2023-08 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, we will explore in the world of Data Science and inside you will gain informative insights in depth. You wiill access Maths needed for Data Science in detail with the formulase, examples and simple explanations. Then you will go through Python needed for Data Science, where you will get everything in Python from basics to advanced level, code examples and explanations. And the main thing is Machine Learning, here Machine Learning Basics to advanced techniques, everything is explained well. Access everything in detail and go deep inside each concept, understand them well and gain informative insights. Unlock the full potential of data science with "Data Science Fusion: Integrating Maths, Python, and Machine Learning." This comprehensive guide empowers you to master the essential components of data science, equipping you with the knowledge and skills to tackle real-world challenges. Begin your journey by understanding the core principles of data science and its vast applications. Embrace Python, the preferred language in the field, and discover the power of essential libraries for data manipulation, visualization, and analysis. Delve into the mathematical foundations that underpin data analysis and machine learning, including linear algebra, calculus, and statistics. With a solid grasp of both mathematics and Python, dive into the exciting realm of machine learning. Learn about supervised and unsupervised learning, and explore the cutting-edge techniques of deep learning and natural language processing. What sets this book apart is its emphasis on the fusion of mathematical theory with practical Python implementation. Each concept is accompanied by hands-on projects and real-world examples, bridging the gap between theory and application. Whether you're an absolute beginner or an experienced practitioner, with insights into model deployment, evaluation, and ethical considerations, this book prepares you to make informed decisions in the data-driven world. Unleash the true potential of data science and revolutionize your understanding of mathematics, Python, and machine learning in the data-driven era.

Data Science and Machine Learning

Author : Dirk P. Kroese
Publisher : CRC Press
ISBN 13 : 9781138492530
Total Pages : 510 pages
Book Rating : 4.4/5 (925 download)

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Book Synopsis Data Science and Machine Learning by : Dirk P. Kroese

Download or read book Data Science and Machine Learning written by Dirk P. Kroese and published by CRC Press. This book was released on 2019 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The purpose of this book is to provide an accessible, yet comprehensive, account of data science and machine learning. It is intended for anyone interested in gaining a better understanding of the mathematics and statistics that underpin the rich variety of ideas and machine learning algorithms in data science"--

Statistical Learning and Data Science

Author : Mireille Gettler Summa
Publisher : Chapman & Hall/CRC
ISBN 13 : 9780367381899
Total Pages : 0 pages
Book Rating : 4.3/5 (818 download)

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Book Synopsis Statistical Learning and Data Science by : Mireille Gettler Summa

Download or read book Statistical Learning and Data Science written by Mireille Gettler Summa and published by Chapman & Hall/CRC. This book was released on 2019-09-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Driven by a vast range of applications, data analysis and learning from data are vibrant areas of research. Various methodologies, including unsupervised data analysis, supervised machine learning, and semi-supervised techniques, have continued to develop to cope with the increasing amount of data collected through modern technology. With a focus on applications, this volume presents contributions from some of the leading researchers in the different fields of data analysis. Synthesizing the methodologies into a coherent framework, the book covers a range of topics, from large-scale machine learning to synthesis objects analysis.

Statistical Inference and Machine Learning for Big Data

Author : Mayer Alvo
Publisher :
ISBN 13 : 9783031067860
Total Pages : 0 pages
Book Rating : 4.0/5 (678 download)

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Book Synopsis Statistical Inference and Machine Learning for Big Data by : Mayer Alvo

Download or read book Statistical Inference and Machine Learning for Big Data written by Mayer Alvo and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Data Fusion and Matching by Maximizing Statistical Dependencies

Author : Abhishek Tripathi
Publisher :
ISBN 13 : 9789521067501
Total Pages : 83 pages
Book Rating : 4.0/5 (675 download)

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Book Synopsis Data Fusion and Matching by Maximizing Statistical Dependencies by : Abhishek Tripathi

Download or read book Data Fusion and Matching by Maximizing Statistical Dependencies written by Abhishek Tripathi and published by . This book was released on 2011 with total page 83 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Chemometrics and Numerical Methods in LIBS

Author : Vincenzo Palleschi
Publisher : John Wiley & Sons
ISBN 13 : 1119759587
Total Pages : 388 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Chemometrics and Numerical Methods in LIBS by : Vincenzo Palleschi

Download or read book Chemometrics and Numerical Methods in LIBS written by Vincenzo Palleschi and published by John Wiley & Sons. This book was released on 2022-10-25 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chemometrics and Numerical Methods in LIBS A practical guide to the application of chemometric methods to solve qualitative and quantitative problems in LIBS analyses Chemometrics and Numerical Methods in LIBS, delivers an authoritative and practical exploration of the use of advanced chemometric methods to laser-induced breakdown spectroscopy (LIBS) cases. The book discusses the fundamentals of chemometrics before moving on to solutions that can be applied to data analysis methods. It is a concise guide designed to help readers at all levels of knowledge solve commonly encountered problems in the field. The book includes three sections: LIBS information simplification, LIBS classification, and quantitative analysis by LIBS. Each section of the book is divided into a description of relevant techniques and practical examples of its applications. Contributors to this edited volume are the most recognized international experts on the chemometric techniques relevant to LIBS analysis. Chemometrics and Numerical Methods in LIBS also includes: A thorough introduction to the simplification of LIBS information, including principal component analysis, independent component analysis, and parallel factor analysis Comprehensive explorations of classification by LIBS, including spectral angle mapping, linear discriminant analysis, graph clustering, self-organizing maps, and artifical neural networks Practical discussions of linear methods for quantitative analysis by LIBS, including calibration curves, partial least squares regression, and limit of detection In-depth examinations of multivariate analysis and non-linear methods, including calibration-free LIBS, the non-linear Kalman filter, artificial and convolutional neural networks for quantification Relevant for researchers and PhD students seeking practical information on the application of advanced statistical methods to the analysis of LIBS spectra, Chemometrics and Numerical Methods in LIBS will also earn a place in the libraries of students taking courses involving LIBS spectro-analytical techniques