{"product_id":"9781119600961","title":"Multiblock Data Fusion in Statistics and Machine Learning Applications in the Natural and Life Sciences","description":"\u003ch1\u003eMultiblock Data Fusion in Statistics and Machine Learning\u003c\/h1\u003e\u003ch2\u003eApplications in the Natural and Life Sciences\u003c\/h2\u003e\u003ch3\u003eAge K. Smilde | Tormod Næs | Kristian Hovde Liland\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eScience \/ Chemistry \/ Analytic\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cb\u003eMultiblock Data Fusion in Statistics and Machine Learning\u003c\/b\u003e \u003cp\u003e\u003cb\u003eExplore the advantages and shortcomings of various forms of multiblock analysis, and the relationships between them, with this expert guide \u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eArising 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.  \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eMultiblock Data Fusion in Statistics and Machine Learning: Applications in the Natural and Life Sciences\u003c\/i\u003e 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.  \u003c\/p\u003e\n\u003cp\u003e 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.  \u003c\/p\u003e\n\u003cp\u003e This book includes:  \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eA thorough introduction to the different options available for the fusion of multiple data sets, including methods originating in psychometrics and chemometrics\u003c\/li\u003e \u003cli\u003ePractical discussions of well-known and lesser-known methods with applications in a wide variety of data problems\u003c\/li\u003e \u003cli\u003eIncluded, functional R-code for the application of many of the discussed methods\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e Perfect for graduate students studying data analysis in the context of the natural and life sciences, including bioinformatics, sensometrics, and chemometrics, \u003ci\u003eMultiblock Data Fusion in Statistics and Machine Learning: Applications in the Natural and Life Sciences\u003c\/i\u003e is also an indispensable resource for developers and users of the results of multiblock methods.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e \u003cp\u003e\u003cb\u003eAge K. Smilde\u003c\/b\u003e is a Professor of Biosystems Data Analysis at the Swammerdam Institute for Life Sciences at the University of Amsterdam. He also holds a part-time position at the Department of Machine Intelligence of Simula Metropolitan Center for Digital Engineering in Oslo, Norway. His research interest is multiblock data analysis and its implementation in different fields of life sciences. He is currently the Editor-in-Chief of the \u003ci\u003eJournal of Chemometrics. \u003c\/i\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003e Tormod Næs\u003c\/b\u003e is a Senior Scientist at Nofima, a food research institute in Norway. He is also currently employed as adjoint professor at the Department of Food Science, University of Copenhagen, Denmark and as extraordinary professor at University of Stellenbosch, South Africa. His main research interest is multivariate analysis with special emphasis on applications in sensory science and spectroscopy.  \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eKristian Hovde Liland\u003c\/b\u003e is an Associate Professor with a top scientist scholarship in Data Science at the Norwegian University of Life Sciences and works in the areas of chemometrics, data analysis and machine learning. His main research is in linear prediction modelling, spectroscopy, and the transition between chemometrics and machine learning. \u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e09 May 2022\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eWiley\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eWiley\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9781119600961\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePage Count: \u003c\/td\u003e\n\u003ctd\u003e416\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWeight (oz): \u003c\/td\u003e\n\u003ctd\u003e36.0\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":44380516548748,"sku":"9781119600961","price":164.66,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9781119600961_c7f917fa-9493-4475-997c-b36571082989.jpg?v=1780155031","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9781119600961","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}