the Simple Tutorial of Machine Learning
2008-04-17 08:58
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Linear Methods for Regression
Linear Methods for Classification
Linear Discriminant Analysis
Logistic Regression
Separating Hyperplanes
Basis Expansions and Regularization
Kernel Methods
Model Assessment and Selection
Model Inference and Averaging
Boostrapping
EM Algorithm
MCMC for Sampling fromthe Posterior
Bagging
Model Averaging and Stacking
Stochastic Search: Bumping
Additive Models, Trees, and Related Methods
Generalized Additive Models
Tree-Based Methods
MARS: Multivariate Adaptive Regression Splines
Boosting and Additive Trees
Neural Networks
Support Vector Machines and Flexible Discriminants
Prototype Methods and Nearest-Neighbors
Unsupervised Learning
Association Rules
Cluster Analysis
Principal Components, Curves and Surfaces
Independent Component Analysis and Exploratory Projection Pursuit
Multidimensional Scaling
Linear Methods for Classification
Linear Discriminant Analysis
Logistic Regression
Separating Hyperplanes
Basis Expansions and Regularization
Kernel Methods
Model Assessment and Selection
Model Inference and Averaging
Boostrapping
EM Algorithm
MCMC for Sampling fromthe Posterior
Bagging
Model Averaging and Stacking
Stochastic Search: Bumping
Additive Models, Trees, and Related Methods
Generalized Additive Models
Tree-Based Methods
MARS: Multivariate Adaptive Regression Splines
Boosting and Additive Trees
Neural Networks
Support Vector Machines and Flexible Discriminants
Prototype Methods and Nearest-Neighbors
Unsupervised Learning
Association Rules
Cluster Analysis
Principal Components, Curves and Surfaces
Independent Component Analysis and Exploratory Projection Pursuit
Multidimensional Scaling
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