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Diverse Applications of Principal Component Analysis

This book emphasizes on the diverse applications of principal component analysis. The aim of this book is to enhance knowledge;

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Principal Component Neural Networks

Systematically explores the relationship between principal component analysis (PCA) and neural networks. Provides a synergistic examination;

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Principal Component Analysis

The first edition of this book was the first comprehensive text written solely on principal component analysis. The second edition updates;

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Principal Component Analysis

The first edition of this book was the first comprehensive text written solely on principal component analysis. The second edition updates;

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Face Recognition

control systems. Topics discussed in this compilation include two-dimensional principal component analysis algorithms for face recognition;

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Principal Component Analysis

Principal Component Analysis is een boek van Nova Science Publishers;

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Principal Component Analysis

analysis in data analysis. Principal component analysis involves a statistical procedure which orthogonally transforms a set of possibly correlated;

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Principal Component Analysis

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Principal Component Analysis

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Principal Component Analysis

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Principal Component Analysis Handbook

This book on Principal component analysis (PCA) is a significant contribution to the field of data analysis. PCA involves a statistical;

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Constrained Principal Component Analysis and Related Techniques

In multivariate data analysis, regression techniques predict one set of variables from another while principal component analysis (PCA;

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Constrained Principal Component Analysis and Related Techniques

In multivariate data analysis, regression techniques predict one set of variables from another while principal component analysis (PCA;

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Computational Methods With Applications in Bioinformatics Analysis

, principal component analysis, fuzzy integral, graph-based ensemble clustering method, semantic analysis, interolog approach, molecular simulations;

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Nonlinear Principal Component Analysis and Its Applications

This book expounds the principle and related applications of nonlinear principal component analysis (PCA), which is useful method to;

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Sparse representation of High Dimensional Data for Classification

In this book you will find the use of sparse Principal Component Analysis (PCA) for representing high dimensional data for classification;

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Independent Component Analysis

decomposition than other well-known models such as principal component analysis. This self-contained book contains a structured series of edited papers;

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Robust Methods for Data Reduction

important and useful methods in practical applications. The main areas covered include principal components analysis, sparse principal component;

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Robust Methods for Data Reduction

important and useful methods in practical applications. The main areas covered include principal components analysis, sparse principal component;

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Permutation Tests in Shape Analysis

shapes from a (possibly random) sample and to estimate shape variability in a sample[1]. One of the main methods used is principal component;

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New Developments in Psychometrics

independent and principal component analysis. The 80 papers collected here provide a valuable source of information for all who are concerned with;

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Computational Modelling of Objects Represented in Images III

element method, principal component analysis, stochastic methods, neural networks and fuzzy logic. The book is useful to researchers and students;

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Automated Incident Detection in Surveillance Systems

feature set to be extracted, and the appropriate choice of algorithm. We investigate methods based upon principal component analysis and nearest;

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A Biologist's Guide to Analysis of DNA Microarray Data

is covered, including cluster analysis, function prediction, and principal component analysis. A thorough, critical review of software programs;

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Applied Multivariate Statistical Analysis - Summaries of theory and Exercises solved

summaries, and exercises solved, on the following topics: Multiple Linear Regression, Principal Component Analysis (without and with Varimax;

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Market Risk Analysis Vol 2

this volume include:* Factor analysis with orthogonal regressions and using principal component factors;* Estimation of symmetric and;

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Spatial Analysis

of entities, spatial dependency and spatial auto-correlation problem, scaling, spatial sampling, etc. The principal types of spatial analysis are;

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