Chapter 21 Methods and algorithms of machine learning

Regression Analysis
* Ordinary Least Squares Regression (OLSR)
* Linear Regression
* Logistic Regression
* Stepwise Regression
* Polynomial Regression
* Locally Estimated Scatterplot Smoothing (LOESS)

Distance-based algorithms
* k-Nearest Neighbor (kNN)
* Learning Vector Quantization (LVQ)
* Self-organizing Map (SOM)

Regularization Algorithms
* Ridge Regression
* Least Absolute Shrinkage and Selection Operator (LASSO)
* Elastic Net
* Least-Angle Regression (LARS)

Decision Tree Algorithms
* Classification and Regression Tree (CART)
* Iterative Dichotomiser 3 (ID3)
* C4.5 and C5.0
* Chi-squared Automatic Interation Detection (CHAID)
* Random Forest
* Conditional Decision Trees

Bayesian Algorithms
* Naive Bayes
* Gaussian Naive Bayes
* Multinomial Naive Bayes
* Bayesian Belief Network (BBN)
* Bayesian Network (BN)

Clustering Algorithms
* k-Means
* k-Medians
* Partitioning Around MEdoids (PAM)
* Hierarchical Clustering

Association Rule Mining Algorithms
* Apriori algorithm
* Eclat algorithm
* FP-growth algorithm
* Context Based Rule Mining

Artifical Neural Network Algorithms
* Perceptron
* Back-Propagation
* Hopfield Network
* Radial Basis Function Network (RBFN)

Deep Learining Algorithms
* Deep Boltzmann Machine (DBM)
* Deep Belief Networks (DBN)
* Convolutional NEural Network (CNN)
* Stacked Auto-Encoders

Dimensionality Reduction Algorithms
* Principal Component Analysis (PCA)
* Principal Compnent Regression (PCR)
* Partial LEast Squares Regression (PLSR)
* Multidimensional Scaling (MDS)
* Linear Discriminant Analysis (LDA)
* Mixtrue Discriminant Analysis (MDA)
* Quadratic Discriminant Analysis (QDA) 1. PCA (linear)
2. t-SNE (non-parametric/ nonlinear)
3. Sammon mapping (nonlinear)
4. Isomap (nonlinear)
5. LLE (nonlinear)
6. CCA (nonlinear)
7. SNE (nonlinear)
8. MVU (nonlinear)
9. Laplacian Eigenmaps (nonlinear)

Ensemble Algorithms
* Boosting
* Bagging
* AdaBoost
* Stacked Generalization (blending)
* Gradient Boosting Machines (GBM)

Text Mining
* Automatic summarization
* Named entity recognition (NER)
* Optical character recognition (OCR)
* Part-of-speech tagging * Sentiment analysis * Speech recognition * Topic Modeling