New Classification Techniques

Author : G. Peck
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781979472289
Total Pages : 396 pages
Book Rating : 4.4/5 (722 download)

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Download or read book Data Science With Matlab. Classification Techniques written by G. Peck and published by Createspace Independent Publishing Platform. This book was released on 2017-11-06 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book develops Descriptive Classification Techniques (Cluster Analysis) and Predictive Classification Techniques (Decision Trees, Discriminant Analysis and Naive bayes and Neural Networks). In addition, the book also develops Classification Learner an Neural Network Techniques. Use the Classification Learner app to train models to classify data using supervisedmachine learning. The app lets you explore supervised machine learning interactivelyusing various classifiers. Automatically train a selection of models and help you choose the best model. Modeltypes include decision trees, discriminant analysis, support vector machines, logistic regression, nearest neighbors, and ensemble classification. Neural Network Toolbox provides algorithms, pretrained models, and apps to create, train, visualize, and simulate both shallow and deep neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The most important content in this book is the following: - 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 Segmentation - Evaluation Models - 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 - Data Mining and Machine Learning in MATLAB - 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 - 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 - Check Performance in the History List - Plot Classifier Results - Check the ROC Curve - Export Classification Model to Predict New Data - Export the Model to the Workspace to Make Predictions for 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 - 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