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Tensors for Data Processing

Tensors for Data Processing: Theory, Methods and Applications presents both classical and state-of-the-art methods on tensor;

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From Algebraic Structures to Tensors

Nowadays, tensors play a central role for the representation, mining, analysis, and fusion of multidimensional, multimodal, and;

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Anisotropy Across Fields and Scales

properties of tensors and tensor fields, challenges faced in processing and visualizing different types of data, statistical techniques for data;

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Robust Statistics for Signal Processing

. Topics covered include advanced robust methods for complex-valued data, robust covariance estimation, penalized regression models, dependent data;

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Tensors

Tensors are ubiquitous in the sciences. The geometry of tensors is both a powerful tool for extracting information from data sets, and a;

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Theory and Computation of Tensors

on matrices. Data in the Big Data Era is not only growing larger but also becoming much more complicated. Tensors (multi-dimensional arrays;

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Matrix and Tensor Decompositions in Signal Processing

very useful tensor networks for the analysis of massive data. Parametric estimation algorithms will be presented for the identification of the;

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Tensors in Image Processing and Computer Vision

resource for researchers, practitioners and advanced students working in the area of computer vision and image processing.;

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Tensors for Physics

. Cartesian Tensors are needed for the description of directional phenomena in many branches of physics and for the characterization the anisotropy;

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Latent Variable Analysis and Signal Separation

. They were organized in topical sections named: tensor approaches; from source positions to room properties: learning methods for audio scene;

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Theory and Computation of Complex Tensors and its Applications

authors design the neural network model for the computation of the rank-one approximation of real tensors, a normalization algorithm to convert;

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Theory and Computation of Complex Tensors and its Applications

authors design the neural network model for the computation of the rank-one approximation of real tensors, a normalization algorithm to convert;

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Susceptibility Tensors for Nonlinear Optics

Susceptibility Tensors for Nonlinear Optics is a unique and invaluable reference book with accompanying software. Starting from basic;

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Tensor Calculus for Physics A Concise Guide

Understanding tensors is essential for any physics student dealing with phenomena where causes and effects have different directions. A;

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PyTorch Recipes

, you'll get familiarized with tensors, a type of data structure used to calculate arithmetic operations and also learn how they operate. You will;

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Physical Components of Tensors

backgrounds for the theory in the first three chapters. In addition, all field equations are also given in physical components as well. Comprised;

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Advances in Damage Mechanics: Metals and Metal Matrix Composites With an Introduction to Fabric Tensors

are solely dedicated to experimental investigations conducted by the authors. In its last chapter, the book presents experimental data for;

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Radar Data Processing With Applications

classical theory and development methods of radar data processing Provides state-of-the-art research results, including data processing for modern;

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Query Processing over Uncertain Databases

Due to measurement errors, transmission lost, or injected noise for privacy protection, uncertainty exists in the data of many real;

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Large Scale and Big Data

currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses;

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Large Scale and Big Data

currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses;

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Tensors

basics of quantum information theory, Strassen's laser method for matrix multiplication, and moment maps in algebraic geometry. It also details;

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Multilinear Subspace Learning

Reduction of Multidimensional Data gives a comprehensive introduction to both theoretical and practical aspects of MSL for the dimensionality;

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Algorithmic Aspects of Parallel Data Processing

. While the applications of such systems are diverse (for example, machine learning, data analytics), most involve relatively standard data;

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Processing of Seismic Reflection Data Using MATLAB

This short book is for students, professors and professionals interested in signal processing of seismic data using MATLAB (TM). The step;

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Query Processing over Uncertain Databases

Due to measurement errors, transmission lost, or injected noise for privacy protection, uncertainty exists in the data of many real;

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Learning Tensorflow.js

learning and the web--provides a hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience that includes data;

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