Patterns Identification and Data Mining in Weather and Climate, Hannachi
Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman Название: The Elements of Statistical Learning ISBN: 0387848576 ISBN-13(EAN): 9780387848570 Издательство: Springer Рейтинг: Цена: 10061.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.
Описание: This book emphasizes recent advances in the creation of biometric identification systems for various applications in the field of human activity. The book displays the problems that arise in modern systems of biometric identification, as well as the level of development and prospects for the introduction of biometric technologies.
Описание: This book emphasizes recent advances in the creation of biometric identification systems for various applications in the field of human activity. The book displays the problems that arise in modern systems of biometric identification, as well as the level of development and prospects for the introduction of biometric technologies.
Автор: De Rajat De Et Al Название: Machine Interpretation Of Patterns: Image Analysis And Data Mining ISBN: 9814299189 ISBN-13(EAN): 9789814299183 Издательство: World Scientific Publishing Рейтинг: Цена: 18216.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Offers both theoretical and application points of views, the developments and reviews in various areas of pattern recognition, image processing, machine learning, soft computing, data mining and web intelligence. This title is suitable for professionals and advanced graduates in computer science, mathematics and life sciences.
Автор: Timothy Masters Название: Data Mining Algorithms in C++ ISBN: 148423314X ISBN-13(EAN): 9781484233146 Издательство: Springer Рейтинг: Цена: 5487.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Discover hidden relationships among the variables in your data, and learn how to exploit these relationships. This book presents a collection of data-mining algorithms that are effective in a wide variety of prediction and classification applications. All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code. Many of these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the focus is on practical applicability, with all code written in such a way that it can easily be included into any program. The Windows-based DATAMINE program lets you experiment with the techniques before incorporating them into your own work. What you'll learn
Monte-Carlo permutation tests provide statistically sound assessment of relationships present in your data.
Combinatorially symmetric cross validation reveals whether your model has true power or has just learned noise by overfitting the data.
Feature weighting as regularized energy-based learning ranks variables according to their predictive power when there is too little data for traditional methods.
The eigenstructure of a dataset enables clustering of variables into groups that exist only within meaningful subspaces of the data.
Plotting regions of the variable space where there is disagreement between marginal and actual densities, or where contribution to mutual information is high, provides visual insight into anomalous relationships.
Who this book is for
The techniques presented in this book and in the DATAMINE program will be useful to anyone interested in discovering and exploiting relationships among variables. Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.
Автор: Sergio Bittanti; Giorgio Picci Название: Identification, Adaptation, Learning ISBN: 3642082483 ISBN-13(EAN): 9783642082481 Издательство: Springer Рейтинг: Цена: 34029.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Appice Название: New Frontiers in Mining Complex Patterns ISBN: 3319786792 ISBN-13(EAN): 9783319786797 Издательство: Springer Рейтинг: Цена: 6097.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book features a collection of revised and significantly extended versions of the papers accepted for presentation at the 6th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2017, held in conjunction with ECML-PKDD 2017 in Skopje, Macedonia, in September 2017.
Описание: This book sheds light on the challenges facing social media in combating malicious accounts, and aims to introduce current practices to address the challenges.
Автор: Wei Wang; Jiong Yang Название: Mining Sequential Patterns from Large Data Sets ISBN: 1441937072 ISBN-13(EAN): 9781441937070 Издательство: Springer Рейтинг: Цена: 15855.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Valliappa Lakshmanan; Eric Gilleland; Amy McGovern Название: Machine Learning and Data Mining Approaches to Climate Science ISBN: 3319172190 ISBN-13(EAN): 9783319172194 Издательство: Springer Рейтинг: Цена: 22797.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents innovative work in Climate Informatics, a new field that reflects the application of data mining methods to climate science, and shows where this new and fast growing field is headed.
Автор: Zhang Zhihua, Li Jianping Название: Big Data Mining for Climate Change ISBN: 0128187034 ISBN-13(EAN): 9780128187036 Издательство: Elsevier Science Рейтинг: Цена: 19875.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Climate change mechanisms, impacts, risks, mitigation, adaption, and governance are widely recognized as the biggest, most interconnected problem facing humanity. Big Data Mining for Climate Change addresses one of the fundamental issues facing scientists of climate or the environment: how to manage the vast amount of information available and analyse it. The resulting integrated and interdisciplinary big data mining approaches are emerging, partially with the help of the United Nation's big data climate challenge, some of which are recommended widely as new approaches for climate change research. Big Data Mining for Climate Change delivers a rich understanding of climate-related big data techniques and highlights how to navigate huge amount of climate data and resources available using big data applications. It guides future directions and will boom big-data-driven researches on modeling, diagnosing and predicting climate change and mitigating related impacts.
This book mainly focuses on climate network models, deep learning techniques for climate dynamics, automated feature extraction of climate variability, and sparsification of big climate data. It also includes a revelatory exploration of big-data-driven low-carbon economy and management. Its content provides cutting-edge knowledge for scientists and advanced students studying climate change from various disciplines, including atmospheric, oceanic and environmental sciences; geography, ecology, energy, economics, management, engineering, and public policy.
Provides a step-by-step guide for applying big data mining tools to climate and environmental research
Presents a comprehensive review of theory and algorithms of big data mining for climate change
Includes current research in climate and environmental science as it relates to using big data algorithms
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