Machine Learning With Matlab. Unsupervised Learning Techniques: Classification
- Length: 489 pages
- Edition: 1
- Language: English
- Publisher: lulu.com
- Publication Date: 2020-11-19
- ISBN-10: B08VW4LN11
- Sales Rank: #3520757 (See Top 100 Books)
Machine learning uses two types of techniques: Supervised Learning techniques (predictive techniques), which trains a model on known input and output data so that it can predict future outputs, and Supervised Learning techniques (descriptive techniques), which finds hidden patterns or intrinsic structures in input data. Unsupervised learning techniques finds hidden patterns or intrinsic structures in data. It is used to draw inferences from datasets consisting of input data without labeled responses. Clustering is the most common descriptive technique. It is used for exploratory data analysis to find hidden patterns or groupings in data. Applications for clustering include gene sequence analysis, market research, and object recognition. This book develops classification unsupervised learning techniques.
MACHINE LEARNING TECHNIQUES 1.1 MACHINE LEARNING. INTRODUCTION 1.1.1 Machine Learning Techniques with Matlab 1.1.2 Train Classification Models in Classification Learner App 1.1.3 Train Regression Models in Regression Learner App 1.1.4 Train Neural Networks for Deep Learning UNSUPERVISED LEARNING TECHNIQUES. CLASSIFICATION WITH HIERARCHICAL CLUSTERING 2.1 CLASSIFICATION WITH CLUSTER ANALYSYS 2.2 Hierarchical Clustering 2.2.1 Introduction to Hierarchical Clustering 2.2.2 Algorithm Description 2.2.3 Similarity Measures 2.2.4 Linkages 2.2.5 Dendrograms 2.2.6 Verify the Cluster Tree 2.2.7 Create Clusters 2.3 FUNCTIONS FOR HIERARCHICAL CLUSTERING 2.3.1 Functions 2.3.2 cluster 2.3.3 clusterdata 2.3.4 cophenet 2.3.5 inconsistent 2.3.6 linkage 2.3.7 pdist 2.3.8 squareform unsupervised learning techniques. classification with NON HIERARCHICAL CLUSTERING 3.1 classification with NON HIERARCHICAL CLUSTERING 3.2 k-Means Clustering 3.2.1 Introduction to k-Means Clustering 3.2.2 Create Clusters and Determine Separation 3.2.3 Determine the Correct Number of Clusters 3.2.4 Avoid Local Minima 3.3 MATLAB Functions FOR NON HIERARCHICAL CLUSTERING 3.3.1 kmeans 3.3.2 kmedoids 3.3.3 mahal CLUSTERING USING GAUSSIAN MIXTURE MODELS AND HIDDEN MARKOV MODELS 4.1 Gaussian Mixture Models 4.2 Clustering Using Gaussian Mixture Models 4.2.1 How Gaussian Mixture Models Cluster Data 4.2.2 Covariance Structure Options 4.2.3 Effects of Initial Conditions 4.2.4 When to Regularize 4.3 Cluster Data from Mixture of Gaussian Distributions 4.3.1 Simulate Data from a Mixture of Gaussian Distributions 4.3.2 Fit the Simulated Data to a Gaussian Mixture Model 4.3.3 Cluster the Data Using the Fitted GMM 4.3.4 Estimate Cluster Membership Posterior Probabilities 4.3.5 Assign New Data to Clusters 4.4 Cluster Gaussian Mixture Data Using Soft Clustering 4.5 Tune Gaussian Mixture Models 4.6 Gaussian Mixture Models FUNCTIONS 4.6.1 fitgmdist 4.6.2 cluster 4.6.3 posterior 4.6.4 gmdistribution 4.7 Markov Chains 4.8 Hidden Markov Models (HMM) 4.8.1 Introduction to Hidden Markov Models (HMM) 4.8.2 Analyzing Hidden Markov Models non supervised learning techniques CLASSIFICATION with NEAREST NEIGHBORS. KNN CLASSIFIERS 5.1 Classification Using Nearest Neighbors 5.1.1 Pairwise Distance Metrics 5.1.2 k-Nearest Neighbor Search and Radius Search 5.1.3 Classify Query Data 5.1.4 Find Nearest Neighbors Using a Custom Distance Metric 5.2 K-Nearest Neighbor Classification for Supervised Learning 5.2.1 Construct KNN Classifier 5.2.2 Examine Quality of KNN Classifier 5.2.3 Predict Classification Using KNN Classifier 5.2.4 Modify KNN Classifier 5.3 Nearest Neighbors FUNCTIONS 5.3.1 ExhaustiveSearcher 5.3.2 KDTreeSearcher 5.3.3 createns CLUSTER VISUALIZATION AND EVALUATION 6.1 INTRODUCTION 6.2 CLUSTER VISUALIZATION 6.2.1 dendrogram 6.2.2 optimalleaforder 6.2.3 manovacluster 6.2.4 silhouette 6.3 CLUSTER EVALUATION 6.3.1 evalclusters 6.3.2 addK 6.3.3 compact 6.3.4 increaseB 6.3.5 plot unsupervised learning techniques. Cluster Data with NEURAL NETWORKS 7.1 NEURAL NETWORK TOOLBOX and deep learning toolbox 7.2 Using Neural Network Toolbox 7.3 Automatic Script Generation 7.4 Neural Network Toolbox Applications 7.5 Neural Network Design Steps 7.6 INTRODUCTION TO CLUSTERING WITH NEURAL NETWORKS 7.7 Using the Neural Network Clustering Tool 7.8 Using Command-Line Functions ClusterIN with Self-Organizing Map Neural Network (SOM KOHONEN NEURAL NETWORK) 8.7.1 One-Dimensional Self-Organizing Map 8.7.2 Two-Dimensional Self-Organizing Map 8.7.3 Training with the Batch Algorithm UNSUPERVISED LEARNIG TECHNIQUES: CLASSIFICATION WITH Self-Organizing Maps. FUNCTIONS 9.1 FUNCTIONS 9.2 nnstart 9.3 view 9.4 selforgmap 9.5 train 9.6 plotsomhits 9.7 plotsomnc 9.8 plotsomnd 9.9 plotsomplanes 9.10 plotsompos 9.11 plotsomtop 9.12 genFunction UNSUPERVISED LEARNING TECHNIQUES. CLASSIFICATION WITH COMPETITIVE NEURAL NETWORKS 10.1 Competitive Layers 10.1.1 10.2 nnstart 10.3 view 10.4 selforgmap 10.5 train 10.6 plotsomhits 10.7 plotsomnc 10.8 plotsomnd 10.9 plotsomplanes 10.10 plotsompos 10.11 plotsomtop unsupervised learning TECHNIQUES. CLASSIFICATION WITH Competitive Layers: FUNCTIONS 11.1 FUNCTIONS TO COMPETITIVE LAYERS 11.2 COMPETLAYER 11.3 view 11.4 trainru 11.5 learnk 11.6 learncon DESCRIPTIVE CLASSIFICATIOn TECHNIQUES. Classify Patterns with a Neural Network 12.1 INTRODUCTION 12.2 Using the Neural Network Pattern Recognition Tool 12.3 Using Command-Line Functions UNSUPERVISED LEARNING TECHNIQUES. CLASSIFICATION WITH NEURAL NETWORKS: EXAMPLES 13.1 Crab Classification 13.1.1 Why Neural Networks? 13.1.2 Preparing the Data 13.1.3 Building the Neural Network Classifier 13.1.4 Testing the Classifier 13.2 Wine Classification 13.2.1 The Problem: Classify Wines 13.2.2 Why Neural Networks? 13.2.3 Preparing the Data 13.2.4 Pattern Recognition with a Neural Network 13.2.5 Testing the Neural Network 13.3 Cancer Detection 13.3.1 Formatting the Data 13.3.2 Ranking Key Features 13.3.3 Classification Using a Feed Forward Neural Network 13.4 Character Recognition 13.4.1 Creating the First Neural Network 13.4.2 Training the first Neural Network 13.4.3 Training the Second Neural Network 13.4.4 Testing Both Neural Networks
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