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Statistical Learning and Data Sciences, Alexander Gammerman; Vladimir Vovk; Harris Papadop


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Цена: 59630.00T
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Автор: Alexander Gammerman; Vladimir Vovk; Harris Papadop
Название:  Statistical Learning and Data Sciences
ISBN: 9783319170909
Издательство: Springer
Классификация:



ISBN-10: 3319170902
Обложка/Формат: Paperback
Страницы: 444
Вес: 0.64 кг.
Дата издания: 26.03.2015
Серия: Lecture Notes in Artificial Intelligence
Язык: English
Размер: 234 x 156 x 24
Основная тема: Computer Science
Подзаголовок: Third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: This book constitutes the refereed proceedings of the Third International Symposium on Statistical Learning and Data Sciences, SLDS 2015, held in Egham, Surrey, UK, April 2015.

Advances in Human Factors in Training, Education, and Learning Sciences

Автор: Terence Andre
Название: Advances in Human Factors in Training, Education, and Learning Sciences
ISBN: 3319600176 ISBN-13(EAN): 9783319600178
Издательство: Springer
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Цена: 149060.00 T
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Statistical Mining and Data Visualization in Atmospheric Sciences

Автор: Timothy J. Brown; Paul W. Mielke Jr.
Название: Statistical Mining and Data Visualization in Atmospheric Sciences
ISBN: 1441949747 ISBN-13(EAN): 9781441949745
Издательство: Springer
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Цена: 111790.00 T
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Описание: Statistical Mining and Data Visualization in Atmospheric Sciences brings together in one place important contributions and up-to-date research results in this fast moving area.

Neural Networks and Statistical Learning

Автор: Ke-Lin Du; M. N. S. Swamy
Название: Neural Networks and Statistical Learning
ISBN: 1447170474 ISBN-13(EAN): 9781447170471
Издательство: Springer
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Цена: 95770.00 T
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Описание: Inclusive coverage of all the essential neural network applications in a statistical learning framework makes this a baseline text for students and researchers, with 25 chapters on all the major approaches that include a wealth of examples and exercises.

Information Theory and Statistical Learning

Автор: Frank Emmert-Streib; Matthias Dehmer
Название: Information Theory and Statistical Learning
ISBN: 1441946500 ISBN-13(EAN): 9781441946508
Издательство: Springer
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Цена: 121110.00 T
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Описание: This interdisciplinary text offers theoretical and practical results of information theoretic methods used in statistical learning. It presents a comprehensive overview of the many different methods that have been developed in numerous contexts.

Machine Learning and Statistical Modeling Approaches to Image Retrieval

Автор: Yixin Chen; Jia Li; James Z. Wang
Название: Machine Learning and Statistical Modeling Approaches to Image Retrieval
ISBN: 1475779305 ISBN-13(EAN): 9781475779301
Издательство: Springer
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Цена: 93160.00 T
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Описание: Machine Learning and Statistical Modeling Approaches to Image Retrieval describes several approaches of integrating machine learning and statistical modeling into an image retrieval and indexing system that demonstrates promising results.

Mathematical-statistical models and qualitative theories for economic and social sciences

Название: Mathematical-statistical models and qualitative theories for economic and social sciences
ISBN: 3319548182 ISBN-13(EAN): 9783319548180
Издательство: Springer
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Цена: 139750.00 T
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Описание: This book presents a broad spectrum of problems related to statistics, mathematics, teaching, social science, and economics as well as a range of tools and techniques that can be used to solve these problems.

Information Theory and Statistical Learning

Автор: Frank Emmert-Streib; Matthias Dehmer
Название: Information Theory and Statistical Learning
ISBN: 0387848150 ISBN-13(EAN): 9780387848150
Издательство: Springer
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Цена: 121110.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This interdisciplinary text offers theoretical and practical results of information theoretic methods used in statistical learning. It presents a comprehensive overview of the many different methods that have been developed in numerous contexts.

Graphical Data Analysis with R

Автор: Unwin
Название: Graphical Data Analysis with R
ISBN: 1498715230 ISBN-13(EAN): 9781498715232
Издательство: Taylor&Francis
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Цена: 76550.00 T
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Описание:

See How Graphics Reveal Information

Graphical Data Analysis with R shows you what information you can gain from graphical displays. The book focuses on why you draw graphics to display data and which graphics to draw (and uses R to do so). All the datasets are available in R or one of its packages and the R code is available at rosuda.org/GDA.

Graphical data analysis is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modelling output, and presenting results. This book guides you in choosing graphics and understanding what information you can glean from them. It can be used as a primary text in a graphical data analysis course or as a supplement in a statistics course. Colour graphics are used throughout.


Computational and Statistical Methods for Analysing Big Data with

Автор: Shen Liu
Название: Computational and Statistical Methods for Analysing Big Data with
ISBN: 0128037326 ISBN-13(EAN): 9780128037324
Издательство: Elsevier Science
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Цена: 77470.00 T
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Описание:

Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration.

"Computational and Statistical Methods for Analysing Big Data with Applications" starts with an overview of the era of big data. It then goes onto explain the computational and statistical methods which have been commonly applied in the big data revolution. For each of these methods, an example is provided as a guide to its application. Five case studies are presented next, focusing on computer vision with massive training data, spatial data analysis, advanced experimental design methods for big data, big data in clinical medicine, and analysing data collected from mobile devices, respectively. The book concludes with some final thoughts and suggested areas for future research in big data.

Advanced computational and statistical methodologies for analysing big data are developed.

Experimental design methodologies are described and implemented to make the analysis of big data more computationally tractable.

Case studies are discussed to demonstrate the implementation of the developed methods.

Five high-impact areas of application are studied: computer vision, geosciences, commerce, healthcare and transportation.

Computing code/programs are provided where appropriate.

Information-Statistical Data Mining

Автор: Bon K. Sy; Arjun K. Gupta
Название: Information-Statistical Data Mining
ISBN: 1461347556 ISBN-13(EAN): 9781461347552
Издательство: Springer
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Цена: 111790.00 T
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Описание: Information-Statistical Data Mining: Warehouse Integration with Examples of Oracle Basics is written to introduce basic concepts, advanced research techniques, and practical solutions of data warehousing and data mining for hosting large data sets and EDA.

Statistical Modeling, Analysis and Management of Fuzzy Data

Автор: Carlo Bertoluzza; Maria A. Gil; Dan A. Ralescu
Название: Statistical Modeling, Analysis and Management of Fuzzy Data
ISBN: 3790825018 ISBN-13(EAN): 9783790825015
Издательство: Springer
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Цена: 153720.00 T
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Описание: The contributions in this book state the complementary rather than competitive relationship between Probability and Fuzzy Set Theory and allow solutions to real life problems with suitable combinations of both theories.

Computer Age Statistical Inference

Автор: Bradley Efron and Trevor Hastie
Название: Computer Age Statistical Inference
ISBN: 1107149894 ISBN-13(EAN): 9781107149892
Издательство: Cambridge Academ
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Цена: 60190.00 T
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Описание: The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.


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