Principal Component Analysis Networks and Algorithms, Kong Xiangyu, Hu Changhua, Duan Zhansheng

Автор: Jolliffe I.T.
Название: Principal Component Analysis. 2 ed.
ISBN: 0387954422 ISBN-13(EAN): 9780387954424
Издательство: Springer
Рейтинг:
Цена: 232910.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The first edition of this book was the first comprehensive text written solely on principal component analysis. The second edition updates and substantially expands the original version, and is once again the definitive text on the subject. Its length is nearly double that of the first edition.

Автор: Cormen, Thomas H., E
Название: Introduction to algorithms 3 ed.
ISBN: 0262033844 ISBN-13(EAN): 9780262033848
Издательство: MIT Press
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Цена: 183920.00 T
Наличие на складе: Нет в наличии.
Описание: A new edition of the essential text and professional reference, with substantial new material on such topics as vEB trees, multithreaded algorithms, dynamic programming, and edge-base flow.

Автор: Van Mieghem, Piet.
Название: Performance analysis of complex networks and systems /
ISBN: 1107058600 ISBN-13(EAN): 9781107058606
Издательство: Cambridge Academ
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Цена: 83430.00 T
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Описание: A rigorous and self-contained guide to the mathematical, stochastic and graph theoretic methods needed to analyse the performance and robustness of complex networks and systems. Containing problems and solved solutions, the book is ideal for graduate students taking courses in performance analysis.

Автор: Asmussen
Название: Stochastic Simulation: Algorithms and Analysis
ISBN: 038730679X ISBN-13(EAN): 9780387306797
Издательство: Springer
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Цена: 46540.00 T
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Описание: Sampling-based computational methods have become a fundamental part of the numerical toolset of practitioners and researchers across an enormous number of different applied domains and academic disciplines. This book provides a broad treatment of such sampling-based methods , as well as accompanying mathematical analysis of the convergence properties of the methods discussed . The reach of the ideas is illustrated by discussing a wide range of applications and the models that have found wide usage. The first  half of the book focusses on general methods, whereas the second half discusses model-specific algorithms. Given the wide range of  examples, exercises and applications students, practitioners and researchers in  probability, statistics, operations research, economics, finance, engineering  as well as biology and chemistry and physics will find the book of value.  Soren Asmussen is Professor of Applied Probability at Aarhus University, Denmark and Peter Glynn is Thomas Ford Professor of  Engineering at Stanford University. 

Автор: Xiangyu Kong; Changhua Hu; Zhansheng Duan
Название: Principal and Minor Component Analysis Neural Networks to Principal Component Analysis Networks and Algorithms
ISBN: 981102913X ISBN-13(EAN): 9789811029134
Издательство: Springer
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Цена: 139750.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book not only provides a comprehensive introduction to neural-based PCA methods in control science, but also presents many novel PCA algorithms and their extensions and generalizations, e.g., dual purpose, coupled PCA, GED, neural based SVD algorithms, etc.

Автор: Agarwal, Dr Basant, Baka, Benjamin
Название: Hands-On Data Structures and Algorithms with Python 2 ed
ISBN: 1788995570 ISBN-13(EAN): 9781788995573
Издательство: Неизвестно
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Цена: 53940.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data structures help us to organize and align the data in a very efficient way. This book will surely help you to learn important and essential data structures through Python implementation for better understanding of the concepts.

Автор: Mori
Название: Nonlinear Principal Component Analysis and Its Applications
ISBN: 981100157X ISBN-13(EAN): 9789811001574
Издательство: Springer
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Цена: 46570.00 T
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Описание: This book expounds the principle and related applications of nonlinear principal component analysis (PCA), which is useful method to analyze mixed measurement levels data.

Автор: Ganesh R. Naik
Название: Advances in Principal Component Analysis
ISBN: 9811067031 ISBN-13(EAN): 9789811067037
Издательство: Springer
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Цена: 111790.00 T
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Описание: This book reports on the latest advances in concepts and further developments of principal component analysis (PCA), addressing a number of open problems related to dimensional reduction techniques and their extensions in detail.

Автор: I.T. Jolliffe
Название: Principal Component Analysis. 2 ed.
ISBN: 1441929991 ISBN-13(EAN): 9781441929990
Издательство: Springer
Рейтинг:
Цена: 232910.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The first edition of this book was the first comprehensive text written solely on principal component analysis. The second edition updates and substantially expands the original version, and is once again the definitive text on the subject. Its length is nearly double that of the first edition.

Автор: Cross Rebecca
Название: Diverse Applications of Principal Component Analysis
ISBN: 1632401509 ISBN-13(EAN): 9781632401502
Издательство: Неизвестно
Цена: 117960.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.

Автор: Cross Rebecca
Название: Principal Component Analysis Handbook
ISBN: 1632404168 ISBN-13(EAN): 9781632404169
Издательство: Неизвестно
Цена: 117960.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.

Автор: Akutsu Tatsuya
Название: Algorithms for Analysis, Inference, and Control of Boolean Networks
ISBN: 9813233427 ISBN-13(EAN): 9789813233423
Издательство: World Scientific Publishing
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Цена: 95040.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:

The Boolean network (BN) is a mathematical model of genetic networks and other biological networks. Although extensive studies have been done on BNs from a viewpoint of complex systems, not so many studies have been undertaken from a computational viewpoint. This book presents rigorous algorithmic results on important computational problems on BNs, which include inference of a BN, detection of singleton and periodic attractors in a BN, and control of a BN. This book also presents algorithmic results on fundamental computational problems on probabilistic Boolean networks and a Boolean model of metabolic networks. Although most contents of the book are based on the work by the author and collaborators, other important computational results and techniques are also reviewed or explained.