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Combinatorial Maps

in geometric modeling, computational and discrete geometry, computer graphics, and image processing and analysis, the book presents the data structures;

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Combinatorial Maps

in geometric modeling, computational and discrete geometry, computer graphics, and image processing and analysis, the book presents the data structures;

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Geometric and Topological Inference

field of topological data analysis. Building on a rigorous treatment of simplicial complexes and distance functions, this self-contained book;

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Geometric and Topological Inference

field of topological data analysis. Building on a rigorous treatment of simplicial complexes and distance functions, this self-contained book;

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Geometric Data Analysis

This book addresses the most efficient methods of pattern analysis using wavelet decomposition. Readers will learn to analyze data in order;

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Combinatorial Inference in Geometric Data Analysis

Geometric Data Analysis designates the approach of Multivariate Statistics that conceptualizes the set of observations as a Euclidean cloud;

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Combinatorial Inference in Geometric Data Analysis

Geometric Data Analysis designates the approach of Multivariate Statistics that conceptualizes the set of observations as a Euclidean cloud;

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Riemannian Geometric Statistics in Medical Image Analysis

data. Riemannian Geometric Statistics in Medical Image Analysis is a complete reference on statistics on Riemannian manifolds and;

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Geometric Spanner Networks

Spanner Network Problem to showcase a number of useful algorithmic techniques, data structure strategies, and geometric analysis techniques with;

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Handbook of Geometric Analysis, No. 1

Geometric Analysis combines differential equations with differential geometry. An important aspect of geometric analysis is to approach;

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Statistical Learning and Data Sciences

its applications, new frontiers in data analysis for nuclear fusion, and geometric data analysis.;

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Computational Finance

algebraic and geometric tools, it is guided by the logic of science to explore information from financial data without prejudice. In fact, this book;

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Geometric Analysis

This book covers recent advances in several important areas of geometric analysis including extremal eigenvalue problems, mini-max methods;

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Handbook of Variational Methods for Nonlinear Geometric Data

This book covers different, current research directions in the context of variational methods for non-linear geometric data. Each chapter;

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Handbook of Variational Methods for Nonlinear Geometric Data

This book covers different, current research directions in the context of variational methods for non-linear geometric data. Each chapter;

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Mathematical Problems in Data Science

, Google Page Rank, efficient algorithms, hardness of problems, different types of big data, geometric data structures, topological data processing;

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Visualization and Verbalization of Data

and geometric data analysis. Although these topics are viewed from a French perspective, the book makes them understandable to an;

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Topological Data Analysis for Genomics and Evolution

numbers, which are typically easier to work with. Topological data analysis is a rapidly developing subfield that leverages the tools of algebraic;

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Geometry Driven Statistics

, shape analysis, spatial data, climate science, fingerprints, image analysis, computer vision and bioinformatics. The book will appeal to;

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Geometric Science of Information

This book constitutes the refereed proceedings of the Third International Conference on Geometric Science of Information, GSI 2017, held;

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Riemannian Geometry and Geometric Analysis

, connections and curvature; the basic models and tools of geometric analysis, such as harmonic functions, forms, mappings, eigenvalues, the Dirac;

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Geometric Morphometrics for Biologists

of shape analysis to biologists who have a stronger background in biology than in multivariate statistics and matrix algebra. These geometric;

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Riemannian Geometry and Geometric Analysis

emphasises the geometric aspects and integrates it into the context of Riemannian geometry and geometric analysis. It also gives a new presentation;

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Spectral Geometry of Shapes

a big increase in 3D shape data that requires a variety of shape analysis methods, hence the need for this comprehensive resource.

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Geometric Analysis

Institute session on Geometric Analysis. The papers give excellent high-level introductions, suitable for graduate students wishing to enter the;

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Linear Algebra for Pattern Processing

accompanied with geometric interpretation. For example, we can think of one data set being orthogonal to another and define a distance between;

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Some Applications of Geometric Thinking

from all parts of secondary school and beyond-algebra, number theory, arithmetic, and data analysis-and as a locus for applications of results;

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