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 |  موضوع: كتاب Pattern Recognition and Classification Using Matlab السبت 06 نوفمبر 2021, 10:24 pm | |
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أخواني في الله
أحضرت لكم كتاب
Pattern Recognition and Classification Using Matlab
K. Taylor
و المحتوى كما يلي :
Contents
Pattern Recognition
1.1 Pattern Recognition Concepts
1.2 Pattern Recognition and Machine Learning
1.3 Probabilistic Classifiers
1.4 Algorithms
1.4.1 Supervised Algorithms Predicting Categorical Labels
1.5 Characteristics of Classification and Pattern
Recognition Algorithms
Parametric Pattern Recognition. Discriminant Analysis
With Matlab
2.1 What Is Discriminant Analysis?
2.2 Create Discriminant Analysis Classifiers
2.3 Creating a Classifier Using Fitcdiscr
2.3.1 Weighted Observations
2.4 How the Predict Method Classifies
2.4.1 Posterior Probability
2.4.2 Prior Probability
2.4.3 Cost2.5 Create and Visualize Discriminant Analysis
Classifier
2.6 Improve a Discriminant Analysis Classifier
2.6.1 Deal with Singular Data
2.6.2 Choose a Discriminant Type
2.6.3 Examine the Resubstitution Error and Confusion Matrix
2.6.4 Cross Validation
2.6.5 Change Costs and Priors
2.7 REGULARIZE A DISCRIMINANT ANALYSIS CLASSIFIER
2.7.1 Load data and create a classifier.
2.7.2 Cross validate the classifier.
2.7.3 Examine the quality of the regularized classifiers.
2.7.4 Choose an optimal tradeoff between model size and accuracy.
2.7.5 Set the regularization parameters.
2.7.6 Heat map plot
2.8 EXAMINE THE GAUSSIAN MIXTURE ASSUMPTION
2.8.1 Bartlett Test of Equal Covariance Matrices for Linear Discriminant
Analysis
2.8.2 Q-Q Plot
2.8.3 Mardia Kurtosis Test of Multivariate Normality
2.9 MATLAB FUNCTIOS POR DISCRIMINANT ANALYSIS
2.9.1 fitcdiscr
2.9.2 predict
2.9.3 templateDiscriminant
2.10 TRAIN DISCRIMINANT ANALYSIS CLASSIFIERS USING
CLASSIFICATION LEARNER APP
NON PARAMETRIC PATTERN RECOGNITION. CLASSIFICATION
TREES
3.1 DECISION TREES
3.2 TRAIN CLASSIFICATION TREE
3.3 TRAIN REGRESSION TREE3.4 VIEWING A CLASSIFICATION OR REGRESSION TREE
3.5 HOW THE FIT METHODS CREATE TREES
3.6 PREDICTION USING CLASSIFICATION AND REGRESSION
TREES
3.7 PREDICT OUT-OF-SAMPLE RESPONSES OF SUBTREES
3.8 IMPROVING CLASSIFICATION TREES AND REGRESSION
TREES
3.8.1 Examining Resubstitution Error
3.8.2 Cross Validation
3.8.3 Choose Split Predictor Selection Technique
3.8.4 Control Depth or "Leafiness"
3.8.5 Pruning
3.9 ALTERNATIVE: CLASSREGTREE
3.9.1 Train Classification Trees Using classregtree
3.9.2 Train Regression Trees Using classregtree
3.10 MATLAB FUNCTIONS FOR DECISION TREES
3.10.1 fitctree
3.10.2 predict
3.10.3 templateTree
3.11 TRAIN DECISION TREES USING CLASSIFICATION LEARNER
APP
SUPPORT VECTOR MACHINE CLASSIFICATION
4.1 SUPPORT VECTOR MACHINE
4.1.1 Separable Data
4.1.2 Nonseparable Data
4.1.3 Nonlinear Transformation with Kernels
4.2 USING SUPPORT VECTOR MACHINES
4.2.1 Training an SVM Classifier
4.2.2 Classifying New Data with an SVM Classifier
4.2.3 Tuning an SVM Classifier
4.2.4 Train SVM Classifiers Using a Gaussian Kernel4.2.5 Train SVM Classifier Using Custom Kernel
4.2.6 Optimize a Cross-Validated SVM Classifier Using
4.2.7 Plot Posterior Probability Regions for SVM Classification Models
4.2.8 Analyze Images Using Linear Support Vector Machines
4.3 FUNCTIONS FOR SUPPORT VECTOR MACHINE
CLASSIFICATION
4.3.1 fitcsvm
4.3.2 fitSVMPosterior
4.3.3 predict
4.3.4 templateSVM
4.3.5 fitclinear
4.3.6 templateLinear
4.3.7 fitcecoc
4.3.8 templateECOC
4.4 TRAIN SUPPORT VECTOR MACHINES USING
CLASSIFICATION LEARNER APP
4.5 TRAIN CLASSIFICATION MODELS IN CLASSIFICATION
LEARNER APP
4.5.1 What Is Supervised Machine Learning?
4.5.2 Automated Classifier Training
4.5.3 Manual Classifier Training
4.5.4 Parallel Classifier Training
4.5.5 Compare and Improve Classification Models
4.6 CHOOSE CLASSIFIER OPTIONS
4.6.1 Choose a Classifier Type
4.6.2 Decision Trees
4.6.3 Discriminant Analysis
4.6.4 Logistic Regression
4.6.5 Support Vector Machines
4.6.6 Nearest Neighbor Classifiers
4.6.7 Ensemble Classifiers4.7 ASSESS CLASSIFIER PERFORMANCE IN CLASSIFICATION
LEARNER
4.7.1 Check Performance in the History List
4.7.2 Plot Classifier Results
4.7.3 Check Performance Per Class in the Confusion Matrix
4.7.4 Check the ROC Curve
NAIVE BAYES
5.1 NAIVE BAYES CLASSIFICATION
5.1.1 Supported Distributions
5.2 FUNCTIONS
5.2.1 fitcnb
5.2.2 predict
5.2.3 templateNaiveBayes
CLASSIFICATION ENSEMBLES. BOOSTING, RANDOM FOREST
AND BAGGING
6.1 ENSEMBLE METHODS
6.1.1 Put Predictor Data in a Matrix
6.1.2 Prepare the Response Data
6.1.3 Choose an Applicable Ensemble Method
6.1.4 Set the Number of Ensemble Members
6.1.5 Prepare the Weak Learners
6.1.6 Call fitensemble
6.2 BASIC ENSEMBLE EXAMPLES
6.2.1 Train Classification Ensemble
6.2.2 Train Regression Ensemble
6.2.3 Select Predictors for Random Forests
6.2.4 Test Ensemble Quality
6.2.5 Classification with Imbalanced Data
6.2.6 Classification: Imbalanced Data or Unequal Misclassification Costs
6.2.7 Classification with Many Categorical Levels6.2.8 Surrogate Splits
6.2.9 LPBoost and TotalBoost for Small Ensembles
6.2.10 Ensemble Regularization
6.2.11 Tune RobustBoost
6.2.12 Random Subspace Classification
6.2.13 TreeBagger Examples
6.3 CLASSIFICATION ENSEMBLES FUNCTIONS
6.3.1 fitcensemble
6.3.2 predict
6.3.3 oobPredict
6.3.4 templateEnsemble
6.4 BAGGED CLASSIFICATION TREES FUNCTIONS
6.4.1 TreeBagger
6.4.2 fitcensemble
6.4.3 predict
6.4.4 oobPredict
6.5 MULTICLASS ECOC FUNCTIONS
6.5.1 fitcecoc
6.5.2 CompactClassificationECOC class
6.6 TRAIN ENSEMBLE CLASSIFIERS USING CLASSIFICATION
LEARNER APP
CLASSIFICATION WITH NEAREST NEIGHBORS. KNN
CLASSIFIERS
7.1 CLASSIFICATION USING NEAREST NEIGHBORS
7.1.1 Pairwise Distance Metrics
7.1.2 k-Nearest Neighbor Search and Radius Search
7.1.3 Classify Query Data
7.1.4 Find Nearest Neighbors Using a Custom Distance Metric
7.2 K-NEAREST NEIGHBOR CLASSIFICATION FOR SUPERVISED
LEARNING
7.2.1 Construct KNN Classifier7.2.2 Examine Quality of KNN Classifier
7.2.3 Predict Classification Using KNN Classifier
7.2.4 Modify KNN Classifier
7.3 NEAREST NEIGHBORS FUNCTIONS
7.3.1 ExhaustiveSearcher
7.3.2 KDTreeSearcher
7.3.3 createns
CLASSIFY PATTERNS WITH A NEURAL NETWORK
8.1 NEURAL NETWORK TOOLBOX
8.2 USING NEURAL NETWORK TOOLBOX
8.3 AUTOMATIC SCRIPT GENERATION
8.4 NEURAL NETWORK TOOLBOX APPLICATIONS
8.5 NEURAL NETWORK DESIGN STEPS
8.6 INTRODUCTION TO PATTERNS RECOGNITION WITH
NEURAL NETWORKS
8.7 USING THE NEURAL NETWORK PATTERN RECOGNITION
TOOL
8.8 USING COMMAND-LINE FUNCTIONS
FUNCTIONS FOR PATTERN RECOGNITION AND
CLASSIFICATION WITH NEURAL NETWORKS
9.1 INTRODUCTION
9.2 VIEW NEURAL NETWORK
9.3 PATTERN RECOGNITION AND LEARNING VECTOR
QUANTIZATION
9.3.1 Pattern recognition network: patternnet
9.3.2 Learning vector quantization neural network: lvqnet
9.4 TRAINING OPTIONS AND NETWORK PERFORMANCE
9.4.1 Receiver operating characteristic: roc
9.4.2 Plot receiver operating characteristic: plotroc
9.4.3 Plot classification confusion matrix: plotconfusion
9.4.4 Neural network performance: crossentropy9.4.5 Construct and Train a Function Fitting Network
9.4.6 Create and train Feedforward Neural Network
9.4.7 Create and Train a Cascade Network
9.5 NETWORK PERFORMANCE
9.5.1 Description
9.5.2 Examples
9.6 FIT REGRESSION MODEL AND PLOT FITTED VALUES VERSUS
TARGETS
9.6.1 Description
9.6.2 Examples
9.7 PLOT OUTPUT AND TARGET VALUES
9.7.1 Description
9.7.2 Examples
9.8 PLOT TRAINING STATE VALUES
9.9 PLOT PERFORMANCES
9.10 PLOT HISTOGRAM OF ERROR VALUES
9.10.1 Syntax
9.10.2 Description
9.10.3 Examples
9.11 GENERATE MATLAB FUNCTION FOR SIMULATING NEURAL
NETWORK
9.11.1 Create Functions from Static Neural Network
9.11.2 Create Functions from Dynamic Neural Network
9.12 A COMPLETE EXAMPLE: HOUSE PRICE ESTIMATION
9.12.1 The Problem: Estimate House Values
9.12.2 Why Neural Networks?
9.12.3 Preparing the Data
9.12.4 Fitting a Function with a Neural Network
9.12.5 Testing the Neural Network
9.13 AUTOENCODER CLASS
9.13.1 trainAutoencoder9.13.2 Construct Deep Network Using Autoencoders
9.13.3 decode
9.13.4 encode
9.13.5 predict
9.13.6 stack
MULTILAYER NEURAL NETWORK
10.1 CREATE, CONFIGURE, AND INITIALIZE MULTILAYER
NEURAL NETWORKS
10.1.1 Other Related Architectures
10.2 FUNCTIONS FOR CREATE, CONFIGURE, AND INITIALIZE
MULTILAYER NEURAL NETWORKS
10.2.1 Initializing Weights (init)
10.2.2 feedforwardnet
10.2.3 configure
10.2.4 init
10.2.5 train
10.2.6 trainlm
10.2.7 tansig
10.2.8 purelin
10.2.9 cascadeforwardnet
10.2.10 patternnet
10.3 TRAIN AND APPLY MULTILAYER NEURAL NETWORKS
10.3.1 Training Algorithms
10.3.2 Training Example
10.3.3 Use the Network
10.4 TRAIN ALGORITMS IN MULTILAYER NEURAL NETWORKS
10.4.1 trainbr:Bayesian Regularization
10.4.2 trainscg: Scaled conjugate gradient backpropagation
10.4.3 trainrp: Resilient backpropagation
10.4.4 trainbfg: BFGS quasi-Newton backpropagation10.4.5 traincgb: Conjugate gradient backpropagation with Powell-Beale
restarts
10.4.6 traincgf: Conjugate gradient backpropagation with Fletcher-Reeves
updates
10.4.7 traincgp: Conjugate gradient backpropagation with Polak-Ribiére
updates
10.4.8 trainoss: One-step secant backpropagation
10.4.9 traingdx: Gradient descent with momentum and adaptive learning
rate backpropagation
10.4.10 traingdm: Gradient descent with momentum backpropagation
10.4.11 traingd: Gradient descent backpropagation
CLASSIFICATION WITH NEURAL NETWORKS. EXAMPLES
11.1 CRAB CLASSIFICATION
11.1.1 Why Neural Networks?
11.1.2 Preparing the Data
11.1.3 Building the Neural Network Classifier
11.1.4 Testing the Classifier
11.2 WINE CLASSIFICATION
11.2.1 The Problem: Classify Wines
11.2.2 Why Neural Networks?
11.2.3 Preparing the Data
11.2.4 Pattern Recognition with a Neural Network
11.2.5 Testing the Neural Network
11.3 CANCER DETECTION
11.3.1 Formatting the Data
11.3.2 Ranking Key Features
11.3.3 Classification Using a Feed Forward Neural Network
11.4 CHARACTER RECOGNITION
11.4.1 Creating the First Neural Network
11.4.2 Training the first Neural Network
11.4.3 Training the Second Neural Network11.4.4 Testing Both Neural Networks
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