Multivariate Data Analysis With Matlab PDF Download
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| Author | : A. Vidales |
| Publisher | : Independently Published |
| Total Pages | : 306 |
| Release | : 2019-02-13 |
| Genre | : Mathematics |
| ISBN | : 9781796848144 |
Download Data Science with Matlab. Multivariate Data Analysis Techniques Book in PDF, ePub and Kindle
Multivariate statistical techniques include supervised and unsupervised learning techniques. This book develops supervised analysis techniques such as decision trees and discriminant analysis models. It also develops non-supervised analysis techniques such as cluster analysis, dimension reduction and multidimensional scaling.Multidimensional scaling (MDS) is a set of methods that address all these problems. MDS allows you to visualize how near points are to each other for many kinds of distance or dissimilarity metrics and can produce a representation of your data in a small number of dimensions. MDS does not require raw data, but only a matrix of pairwise distances or dissimilarities.Feature selection reduces the dimensionality of data by selecting only a subset of measured features (predictor variables) to create a model. Selection criteria usually involve the minimization of a specific measure of predictive error for models fit to different subsets. Algorithms search for a subset of predictors that optimally model measured responses, subject to constraints such as required or excluded features and the size of the subset.Factor analysis is a way to fit a model to multivariate data to estimate just this sort of interdependence. In a factor analysis model, the measured variables depend on a smaller number of unobserved (latent) factors. Because each factor might affect several variables in common, they are known as common factors. Each variable is assumed to be dependent on a linear combination of the common factors, and the coefficients are known as loadings. Each measured variable also includes a component due to independent random variability, known as specific variance because it is specific to one variable.Cluster analysis, also called segmentation analysis or taxonomy analysis, creates groups, or clusters, of data. Clusters are formed in such a way that objects in the same cluster are very similar and objects in different clusters are very distinct. Measures of similarity depend on the application.Decision trees, or classification trees and regression trees, predict responses to data. To predict a response, follow the decisions in the tree from the root (beginning) node downto a leaf node. The leaf node contains the response. Classification trees give responses that are nominal, such as 'true' or 'false'. Regression trees give numeric responses. Statistics and Machine Learning Toolbox trees are binary. Each step in a prediction involves checking the value of one predictor (variable).Discriminant analysis is a classification method. It assumes that differen classes generate data based on different Gaussian distributions. Linear discriminant analysis is also known as the Fisher discriminant, named for its inventor.
| Author | : Wendy L. Martinez |
| Publisher | : CRC Press |
| Total Pages | : 430 |
| Release | : 2004-11-29 |
| Genre | : Business & Economics |
| ISBN | : 0203483375 |
Download Exploratory Data Analysis with MATLAB Book in PDF, ePub and Kindle
Exploratory data analysis (EDA) was conceived at a time when computers were not widely used, and thus computational ability was rather limited. As computational sophistication has increased, EDA has become an even more powerful process for visualizing and summarizing data before making model assumptions to generate hypotheses, encompassing larger a
| Author | : Wendy L. Martinez |
| Publisher | : CRC Press |
| Total Pages | : 589 |
| Release | : 2017-08-07 |
| Genre | : Mathematics |
| ISBN | : 1315349841 |
Download Exploratory Data Analysis with MATLAB Book in PDF, ePub and Kindle
Praise for the Second Edition: "The authors present an intuitive and easy-to-read book. ... accompanied by many examples, proposed exercises, good references, and comprehensive appendices that initiate the reader unfamiliar with MATLAB." —Adolfo Alvarez Pinto, International Statistical Review "Practitioners of EDA who use MATLAB will want a copy of this book. ... The authors have done a great service by bringing together so many EDA routines, but their main accomplishment in this dynamic text is providing the understanding and tools to do EDA. —David A Huckaby, MAA Reviews Exploratory Data Analysis (EDA) is an important part of the data analysis process. The methods presented in this text are ones that should be in the toolkit of every data scientist. As computational sophistication has increased and data sets have grown in size and complexity, EDA has become an even more important process for visualizing and summarizing data before making assumptions to generate hypotheses and models. Exploratory Data Analysis with MATLAB, Third Edition presents EDA methods from a computational perspective and uses numerous examples and applications to show how the methods are used in practice. The authors use MATLAB code, pseudo-code, and algorithm descriptions to illustrate the concepts. The MATLAB code for examples, data sets, and the EDA Toolbox are available for download on the book’s website. New to the Third Edition Random projections and estimating local intrinsic dimensionality Deep learning autoencoders and stochastic neighbor embedding Minimum spanning tree and additional cluster validity indices Kernel density estimation Plots for visualizing data distributions, such as beanplots and violin plots A chapter on visualizing categorical data
| Author | : James Ramsay |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 213 |
| Release | : 2009-06-29 |
| Genre | : Computers |
| ISBN | : 0387981853 |
Download Functional Data Analysis with R and MATLAB Book in PDF, ePub and Kindle
The book provides an application-oriented overview of functional analysis, with extended and accessible presentations of key concepts such as spline basis functions, data smoothing, curve registration, functional linear models and dynamic systems Functional data analysis is put to work in a wide a range of applications, so that new problems are likely to find close analogues in this book The code in R and Matlab in the book has been designed to permit easy modification to adapt to new data structures and research problems
| Author | : G. Peck |
| Publisher | : |
| Total Pages | : 334 |
| Release | : 2017-11-06 |
| Genre | : |
| ISBN | : 9781979495660 |
Download Statistics With Matlab Book in PDF, ePub and Kindle
This book develops Advenced Multivariate Analysis Tecniques: Multivariate Linear Regression, Multivariate General Linear Model, Fixed Effects Panel Model with Concurrent Correlation, Longitudinal Analysis, Classification Learner (decision trees, discriminant analysis, support vector machines, logisticregression, nearest neighbors, and ensemble classification), Regression Learner (linear regression models, regression trees, Gaussian processregression models, support vector machines, and ensembles of regression tres), Support Vector Machine and Neural Networks.The most important content in this book is the following:* Multivariate Methods* Multivariate Linear Regression* Multivariate General Linear Model* Fixed Effects Panel Model with Concurrent Correlation* Longitudinal Analysis* Data Mining and Machine Learning in MATLAB* Selecting the Right Algorithm* Train Classification Models in Classification Learner App* Train Regression Models in Regression Learner App* Train Neural Networks for Deep Learning* Automated Classifier Training* Manual Classifier Training* Parallel Classifier Training* Compare and Improve Classification Models* Decision Trees* Discriminant Analysis* Logistic Regression* Support Vector Machines* Nearest Neighbor Classifiers* Ensemble Classifiers* Feature Selection and Feature Transformation Using* Classification Learner App* Investigate Features in the Scatter Plot* Select Features to Include* Transform Features with PCA in Classification Learner* Investigate Features in the Parallel Coordinates Plot* Assess Classifier Performance in Classification Learner* Plot Classifier Results* Check Performance Per Class in the Confusion Matrix* Check the ROC Curve* Export Classification Model to Predict New Data* Make Predictions for New Data* Train Decision Trees Using Classification Learner App* Train Discriminant Analysis Classifiers Using Classification Learner App* Train Logistic Regression Classifiers Using Classification Learner App* Train Support Vector Machines Using Classification Learner App* Train Nearest Neighbor Classifiers Using Classification Learner App* Train Ensemble Classifiers Using Classification Learner App* Train Regression Models in Regression Learner App* Supervised Machine Learning* Automated Regression Model Training* Manual Regression Model Training* Parallel Regression Model Training* Compare and Improve Regression Models* Choose Regression Model Options* Choose Regression Model Type* Linear Regression Models* Regression Trees* Support Vector Machines* Gaussian Process Regression Models* Ensembles of Trees* Feature Selection and Feature Transformation Using Regression Learner App* Investigate Features in the Response Plot* Select Features to Include* Transform Features with PCA in Regression Learner* Assess Model Performance in Regression Learner App* Evaluate Model Using Residuals Plot* Export Regression Model to Predict New Data* Train Regression Trees Using Regression Learner App* Support Vector Machine Regression* Mathematical Formulation of SVM Regression* Solving the SVM Regression Optimization Problem* Shallow Networks for Pattern Recognition, Clustering and Time Series* Fit Data with a Shallow Neural Network* Classify Patterns with a Shallow Neural Network* Cluster Data with a Self-Organizing Map* Shallow Neural Network Time-Series Prediction and Modeling
| Author | : G. Peck |
| Publisher | : |
| Total Pages | : 216 |
| Release | : 2017-11-06 |
| Genre | : |
| ISBN | : 9781979495202 |
Download Statistics With Matlab Book in PDF, ePub and Kindle
This book develops Multivariate Data Analysis Techniques: Reduction of the Dimension Techniques (Principal Components and Factor Analysis), Multidimensional Scaling, Cluster Analysis, Decision Trees, Discriminant Analysis and Naive Bayes). In addition, the book also develops examples and applications relating to such techniques.The most important content in this book is the following:* Reduction of the dimensión* Principal Component Analysis (PCA)* Factor Analysis* Multidimensional Scaling* Nonclassical and Nonmetric Multidimensional Scaling* Classical Multidimensional Scaling* Hierarchical Clustering* Similarity Measures* Linkages* Dendrograms* Verify the Cluster Tree* Create Clusters* k-Means Clustering* Introduction to k-Means Clustering* Create Clusters and Determine Separation* Determine the Correct Number of Clusters* Clustering Using Gaussian Mixture Models* Cluster Data from Mixture of Gaussian Distributions* Cluster Gaussian Mixture Data Using Soft Clustering* Parametric Classificaton* Performance Curves* ROC Curves* Decision Treess* Prediction Using Classification and Regression Trees* Improving Classification Trees and Regression Trees* Cross Validation* Choose Split Predictor Selection Technique* Control Depth or "Leafiness"* Pruning* Discriminant Analysis Classification* Prediction Using Discriminant Analysis Models* Confusion Matrix and cross valdation* Naive Bayes Segmentation
| Author | : Karter J. |
| Publisher | : Createspace Independent Publishing Platform |
| Total Pages | : |
| Release | : 2016-10-13 |
| Genre | : |
| ISBN | : 9781539512004 |
Download Data Analytics Across Multivariate Statistics Methods Using Matlab Book in PDF, ePub and Kindle
Large, high-dimensional data sets are common in the modern era of computer-based instrumentation and electronic data storage. High-dimensional data present many challenges for statistical visualization, analysis, and modeling.Data visualization, of course, is impossible beyond a few dimensions. As a result, pattern recognition, data preprocessing, and model selection must rely heavily on numerical methods. The most important contents of this book are: Multivariate Linear Regression Estimation of Multivariate Regression Models Multivariate General Linear Model Fixed Effects Panel Model with Concurrent Longitudinal Analysis Multidimensional Scaling Procrustes Analysis Feature Selection Feature Transformation Principal Component Analysis (PCA) Factor Analysis Partial Least Squares Regression and Principal Components Regression Cluster Analysis Hierarchical Clustering Algorithm Description Dendrograms k-Means Clustering Gaussian Mixture Models Cluster with Gaussian Mixtures Parametric Classification Discriminant Analysis What Is Discriminant Analysis? Naive Bayes Classification Supported Distributions Performance Curves Nonparametric Supervised Learning Supervised Learning (Machine Learning) Workflow and Algorithms Steps in Supervised Learning (Machine Learning) Characteristics of Algorithms Classification Using Nearest Neighbors Pairwise Distance k-Nearest Neighbor Search and Radius Search K-Nearest Neighbor Classification for Supervised Learning Construct a KNN Classifier Examine the Quality of a KNN Classifier Predict Classification Based on a KNN Classifier Modify a KNN Classifier Classification Trees and Regression Trees What Are Classification Trees and Regression Trees? Creating a Classification Tree Creating a Regression Tree Viewing a Tree How the Fit Methods Create Trees Predicting Responses With Classification and Regression Trees Improving Classification Trees and Regression Trees Splitting Categorical Predictors Challenges in Splitting Multilevel Predictors Pull Left By Purity Principle Component-Based Partitioning One Versus All By Class Ensemble Methods Framework for Ensemble Learning Basic Ensemble Examples Test Ensemble Quality Classification with Imbalanced Data Classification: Imbalanced Data or Unequal Misclassification Costs Classification with Many Categorical Levels Surrogate Splits LPBoost and TotalBoost for Small Ensembles Ensemble Regularization Tuning RobustBoost Random Subspace Classification TreeBagger Examples Ensemble Algorithms Support Vector Machines (SVM) Understanding Support Vector Machines Using Support Vector Machines Nonlinear Classifier with Gaussian Kernel SVM Classification with Cross Validation
| Author | : C Perez |
| Publisher | : Independently Published |
| Total Pages | : 324 |
| Release | : 2019-05-22 |
| Genre | : |
| ISBN | : 9781099696282 |
Download DATA MINING and BIG DATA ANALYTICS with NEURAL NETWORKS Using MATLAB Book in PDF, ePub and Kindle
The availability of large volumes of data (Big Data) and the generalized use of computer tools has transformed research and data analysis, orienting it towards certain specialized techniques encompassed under the generic name of Analytics (Big Data Analytics) that includes Multivariate Data Analysis (MDA), Data Mining and other Business Intelligence techniques.Data Mining can be defined as a process of discovering new and significant relationships, patterns and trends when examining large amounts of data. The techniques of Data Mining pursue the automatic discovery of the knowledge contained in the information stored in an orderly manner in large databases. These techniques aim to discover patterns, profiles and trends through the analysis of data using advanced statistical techniques of multivariate data analysis.The goal is to allow the researcher-analyst to find a useful solution to the problem raised through a better understanding of the existing data.Data Mining uses two types of techniques: predictive techniques, which trains a model on known input and output data so that it can predict future outputs, and descriptive techniques, which finds hidden patterns or intrinsic structures in input data.
| Author | : Wendy L. Martinez |
| Publisher | : CRC Press |
| Total Pages | : 536 |
| Release | : 2010-12-16 |
| Genre | : Business & Economics |
| ISBN | : 9781439812204 |
Download Exploratory Data Analysis with MATLAB, Second Edition Book in PDF, ePub and Kindle
Since the publication of the bestselling first edition, many advances have been made in exploratory data analysis (EDA). Covering innovative approaches for dimensionality reduction, clustering, and visualization, Exploratory Data Analysis with MATLAB®, Second Edition uses numerous examples and applications to show how the methods are used in practice. New to the Second Edition Discussions of nonnegative matrix factorization, linear discriminant analysis, curvilinear component analysis, independent component analysis, and smoothing splines An expanded set of methods for estimating the intrinsic dimensionality of a data set Several clustering methods, including probabilistic latent semantic analysis and spectral-based clustering Additional visualization methods, such as a rangefinder boxplot, scatterplots with marginal histograms, biplots, and a new method called Andrews’ images Instructions on a free MATLAB GUI toolbox for EDA Like its predecessor, this edition continues to focus on using EDA methods, rather than theoretical aspects. The MATLAB codes for the examples, EDA toolboxes, data sets, and color versions of all figures are available for download at http://pi-sigma.info
| Author | : Antonio Siciliano |
| Publisher | : World Scientific |
| Total Pages | : 294 |
| Release | : 2008 |
| Genre | : Computers |
| ISBN | : 9812835547 |
Download MATLAB Book in PDF, ePub and Kindle
The Windows of the Desktop; A Preliminary Approach to Data and M-Files; Scripts and Functions as M-Files; Numerical Arrays; Other Types of Arrays; The Figure Window for Graphics Objects; Plot 2-D and Image; Flow Control; Appendices: MATLAB Functions Categories; MATLAB Functions and Objects Properties; Operators List; A Table of Special Ascii Codes.
