Applied multidimensional scaling and unfolding., Ingwer Borg, Patrick J.F. Groenen, Patrick Mair
Автор: Paul Fieguth Название: Statistical Image Processing and Multidimensional Modeling ISBN: 1461427053 ISBN-13(EAN): 9781461427056 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Examples abound throughout remote sensing (satellite data mapping, data assimilation, climate-change studies, land use), medical imaging (organ segmentation, anomaly detection), computer vision (image classification, segmentation), and other 2D/3D problems (biological imaging, porous media).
Автор: Gelfand, Israel M. Kapranov, Mikhail Zelevinsky, A Название: Discriminants, resultants, and multidimensional determinants ISBN: 0817647708 ISBN-13(EAN): 9780817647704 Издательство: Springer Рейтинг: Цена: 83850.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Deals with the classical theory of resultants and discriminants.
Автор: Cox, Trevor F. Название: Multidimensional scaling ISBN: 1584880945 ISBN-13(EAN): 9781584880943 Издательство: Taylor&Francis Рейтинг: Цена: 142910.00 T Наличие на складе: Невозможна поставка. Описание: Multidimensional scaling covers a variety of statistical techniques in the area of multivariate data analysis. This book comprehensively covers the area, summarizing the mathematical ideas behind the various techniques and illustrating the techniques with real-life examples. It also includes a computer disk containing programs and data sets.
Автор: Ding Название: Fundamentals of Applied Multidimensional Scaling for Educational and Psychological Research ISBN: 3319781715 ISBN-13(EAN): 9783319781716 Издательство: Springer Рейтинг: Цена: 79190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book explores the fundamentals of multidimensional scaling (MDS) and how this analytic method can be used in applied setting for educational and psychological research. The book tries to make MDS more accessible to a wider audience in terms of the language and examples that are more relevant to educational and psychological research and less technical so that the readers are not overwhelmed by equations. The goal is for readers to learn the methods described in this book and immediately start using MDS via available software programs. The book also examines new applications that have previously not been discussed in MDS literature. It should be an ideal book for graduate students and researchers to better understand MDS.Fundamentals of Applied Multidimensional Scaling for Educational and Psychological Research is divided into three parts. Part I covers the basic and fundamental features of MDS models pertaining to applied research applications. Chapters in this section cover the essential features of data that are typically associated with MDS analysis such as preference ration or binary choice data, and also looking at metric and non-metric MDS models to build a foundation for later discussion and applications in later chapters. Part II examines specific MDS models and its applications for education and psychology. This includes spatial analysis methods that can be used in MDS to test clustering effect of items and individual differences MDS model (INDSCAL). Finally, Part III focuses on new applications of MDS analysis in these research fields. These new applications consist of profile analysis, longitudinal analysis, mean-level change, and pattern change. The book concludes with a historical review of MDS development as an analytical method and a look to future directions.
Автор: I. Borg; P. J. F. Groenen Название: Modern Multidimensional Scaling ISBN: 1441920463 ISBN-13(EAN): 9781441920461 Издательство: Springer Рейтинг: Цена: 163040.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The first edition was released in 1996 and has sold close to 2200 copies. Provides an up-to-date comprehensive treatment of MDS, a statistical technique used to analyze the structure of similarity or dissimilarity data in multidimensional space. The authors have added three chapters and exercise sets.
Автор: Adler Stephen L Название: Guide To Pamir, The: Theory And Use Of Parameterized Adaptive Multidimensional Integration Routines ISBN: 9814425036 ISBN-13(EAN): 9789814425032 Издательство: World Scientific Publishing Рейтинг: Цена: 50690.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Suitable for physicists, applied mathematicians, computer scientists, and engineers, this book presents introductory materials, similar to that on the PAMIR website. It also offers details on the use of the programs than is on the website.
Автор: Gintautas Dzemyda; Olga Kurasova; Julius ?ilinskas Название: Multidimensional Data Visualization ISBN: 1489990003 ISBN-13(EAN): 9781489990006 Издательство: Springer Рейтинг: Цена: 102480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents a variety of methods used in multidimensional data visualization. It details new research results and trends in the field, including optimization, artificial neural networks, combinations of algorithms, and parallel computing.
Автор: Achille A. Lemmi; Gianni Betti Название: Fuzzy Set Approach to Multidimensional Poverty Measurement ISBN: 1441941711 ISBN-13(EAN): 9781441941718 Издательство: Springer Рейтинг: Цена: 158380.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This volume brings together advanced thinking on the multidimensional measurement of poverty.
Автор: Jacob Kogan; Charles Nicholas; Marc Teboulle Название: Grouping Multidimensional Data ISBN: 3642066542 ISBN-13(EAN): 9783642066542 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anomaly detection.Kogan and his co-editors have put together recent advances in clustering large and high-dimension data.
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