Ergebnisse für: Linear Dimension Reduction
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This text provides a mathematically rigorous introduction to modern methods of machine learning and data analysis at the advanced undergraduate/beginning graduate level. The book is self-contained and requires minimal mathematical prerequisites. There is a strong focus on learning how and why algori...
This thesis aims to give the macroscopic behaviors for the small deformations of textile structures, as well as mathematical explanations of phenomena observed in real experiments. The textile structure is presented as a woven canvas made of long, thin fibers, crossing each other in a periodic patte...
In 1901, Karl Pearson invented Principal Component Analysis (PCA). Since then, PCA serves as a prototype for many other tools of data analysis, visualization and dimension reduction: Independent Component Analysis (ICA), Multidimensional Scaling (MDS), Nonlinear PCA (NLPCA), Self Organizing Maps (SO...
In 1901, Karl Pearson invented Principal Component Analysis (PCA). Since then, PCA serves as a prototype for many other tools of data analysis, visualization and dimension reduction: Independent Component Analysis (ICA), Multidimensional Scaling (MDS), Nonlinear PCA (NLPCA), Self Organizing Maps (SO...
This book provides an overview of some classical linear methods in Multivariate Data Analysis. This is an old domain, well established since the 1960s, and refreshed timely as a key step in statistical learning. It can be presented as part of statistical learning, or as dimensionality reduction with...
This book provides an overview of some classical linear methods in Multivariate Data Analysis. This is an old domain, well established since the 1960s, and refreshed timely as a key step in statistical learning. It can be presented as part of statistical learning, or as dimensionality reduction with...
This text provides a mathematically rigorous introduction to modern methods of machine learning and data analysis at the advanced undergraduate/beginning graduate level. The book is self-contained and requires minimal mathematical prerequisites. There is a strong focus on learning how and why algori...
This book constitutes the proceedings of the 10th International Workshop on Frontiers in Algorithmics, FAW 2016, held in Qingdao, China, in June/July 2016.      
The 25 full papers presented in this volume were carefully reviewed and selected from 54 submissions. They deal with algor...
This book offers a leisurely introduction to the concepts and methods of machine learning. Readers will learn about classification trees, Bayesian learning, neural networks and deep learning, the design of experiments, and related methods. For ease of reading, technical details are avoided as far as...
This book constitutes the proceedings of the 10th International Workshop on Frontiers in Algorithmics, FAW 2016, held in Qingdao, China, in June/July 2016.      
The 25 full papers presented in this volume were carefully reviewed and selected from 54 submissions. They deal with algor...
The big data tsunami changes the perspective of industrial and academic research in how they address both foundational questions and practical applications. This calls for a paradigm shift in algorithms and the underlying mathematical techniques. There is a need to understand foundational stren...