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This book expounds the principle and related applications of nonlinear principal component analysis (PCA), which is useful method to analyze mixed measurement levels data.
An introduction to the vast array of features and powerful mathematical functions of Mathematica that uses a multitude of clearly presented examples and worked-out problems that enable the reader to learn from the codes and avoids lengthy explanations.
This book helps readers understand the mathematics of machine learning, and apply them in different situations. The book will be of interest to all researchers who intend to use... Læs mere
This book systematically addresses the design and analysis of efficient techniques for independent random sampling.
The focus on doing data mining rather than just reading about data mining is refreshing.The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment.
This book introduces academic researchers and professionals to the basic concepts and methods for characterizing interdependencies of multiple time series in the frequency domain.
This easy-to-follow textbook/reference presents a concise introduction to mathematical analysis from an algorithmic point of view, with a particular focus on applications of analysis and aspects of mathematical modelling.
This proceedings volume contains eight selected papers thatwere presented... Læs mere
This Brief provides a roadmap for the R language and programming environment with signposts to further resources and documentation.