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Data structures based on non-linear relations and data processing methods

The systematic description starts with basic theory and applications of different kinds of data structures, including storage structures;

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Tensors for Data Processing

data processing theories and methods. As a higher-order generalization of a matrix, tensor-based processing can avoid multi-linear;

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Data structures based on linear relations

Data structures is a key course for computer science and related majors. This book presents a variety of practical or engineering cases and;

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Data Structures using Python

on Non-linear Data Structures where Heap, Hash Table, Trie, and Disjoint Sets are discussed. Trees, though a non-linear data structure;

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Generalized Linear Models and Correlated Data Methods

Offers a unified discussion of generalized linear models and correlated data methods Provides information suitable for graduate non;

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Advanced Data Acquisition and Intelligent Data Processing

DAQ and data processing is a basic part of all automated production systems, diagnostic systems, watching over quality of production;

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[Set Data Structures and Algorithms Analysis, Vol 1]2]

The systematic description starts with basic theory and applications of different kinds of data structures, including storage structures;

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Linear and Non-linear Stability Analysis in Boiling Water Reactors

estimate decay ratios using non-linear techniques. Explores the nuclear stability of Boiling Water Reactors based on linear and non;

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Analysis of Doubly Truncated Data

the topic, it provides likelihood-based methods, Bayesian methods, non-parametric methods, and linear regression methods. These procedures can;

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Biosignal Processing

With the rise of advanced computerized data collection systems, monitoring devices, and instrumentation technologies, large and complex;

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Measurement Data Modeling and Parameter Estimation

measurement data. Although the book is based on the authors' research and teaching experience in aeronautics and astronautics data processing, the;

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Measurement Data Modeling and Parameter Estimation

measurement data. Although the book is based on the authors' research and teaching experience in aeronautics and astronautics data processing, the;

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C & Data Structures

It allows the student to write simple programs in C language to solve the problems. The book introduces simple linear and non-linear Data;

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Swarm Intelligence Methods for Statistical Regression

A core task in statistical analysis, especially in the era of Big Data, is the fitting of flexible, high-dimensional, and non-linear models;

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Swarm Intelligence Methods for Statistical Regression

A core task in statistical analysis, especially in the era of Big Data, is the fitting of flexible, high-dimensional, and non-linear models;

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Geometric Science of Information

into the following subjects: statistics on non-linear data; shape space; optimal transport and applications: image processing; optimal;

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Relative Fidelity Processing of Seismic Data

broadband acquisition is the basis for relative fidelity preservation processing and the influence of seismic acquisition on data processing is also;

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Handbook of Variational Methods for Nonlinear Geometric Data

art. Non-linear geometric data arises in various applications in science and engineering. Examples of nonlinear data spaces are diverse;

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Handbook of Variational Methods for Nonlinear Geometric Data

art. Non-linear geometric data arises in various applications in science and engineering. Examples of nonlinear data spaces are diverse;

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New Perspectives in Partial Least Squares and Related Methods

regression, distance based PLS, significance vs. reliability, and non-linear PLS. Finally, these contributions applied PLS methods to data;

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From Algebraic Structures to Tensors

provides an introduction to matrices and tensors of higher-order based on the structures of vector space and tensor space. Some standard algebraic;

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Analysis of Variance, Design, and Regression

Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data, Second Edition presents linear structures for modeling;

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Analysis of Variance, Design, and Regression

Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data, Second Edition presents linear structures for modeling;

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Statistical Methods for Spatial Data Analysis

generalized linear mixed models for spatial data. It succinctly discusses Bayesian hierarchical models and concludes with reviews on simulating random;

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Statistical Methods for Spatial Data Analysis

stochastic processes. It also requires a mindset that focuses on the unique characteristics of spatial data and the development of specialized;

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Interactive Visualization of Large Higher-Order Tetrahedral Data

techniques cannot be applied to non-conforming meshes. Common linear interpolation method cannot faithfully and accurately evaluate the non-linear;

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Information Processing and Management of Uncertainty in Knowledge-Based Systems

practice; aggregation: pre-aggregation functions and other generalizations of monotonicity; aggregation: aggregation of different data structures;

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