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Machine learning, artificial intelligence, knowledge map
Machine learning, artificial intelligence, knowledge map
Machine learning / artificial intelligence knowledge map
You can build a knowledge map of machine learning for yourself, and strive to master every classic machine learning theory and algorithm, which is briefly summarized as follows:
1) Regression algorithm:
Least Square Method (OrdinaryLeast Square)
Logistic Regression
Stepwise Regression
(reduction method)
MultivariateAdaptive Regression Splines
Locally Estimated Scatterplot Smoothing
2) Instance-based algorithm:
k-Nearest Neighbor (KNN)
Learning Vector Quantization (LVQ)
Self-Organizing Map (SOM)
3) Based on the regularization method:
Ridge Regression L2
Least Absolute Shrinkage and Selection Operator (LASSO) L1
Elastic Net
4) Decision tree learning:
Classification and Regression Tree (CART)
Http://blog.csdn.net/m0_37167788/article/details/78794833
ID3 (Iterative Dichotomiser 3)
C4.5
Chi-squared Automatic Interaction Detection (CHAID)
Decision Stump
Random Forest
Multivariate Adaptive Regression Spline (MARS)
Gradient Boosting Machine (GBM)
5) Based on Bayesian method:
Naive Bayesian algorithm
Averaged One-Dependence Estimators (AODE)
Bayesian Belief Network (BBN)
6) Kernel-based algorithm:
Support Vector Machine (SVM)
Radial Basis Function (RBF)
Linear Discriminate Analysis (LDA)
7) Clustering algorithm:
k-Means algorithm
Expectation Maximization (EM)
8) Learning based on association rules:
Apriori algorithm
Eclat algorithm
9) Artificial neural network:
Perceptron Neural Network
Back Propagation
Hopfield Network
Self-Organizing Map (SOM)
Learning Vector Quantization (LVQ);
10) Deep learning:
Restricted Boltzmann Machine (RBN)
Deep Belief Networks (DBN)
Convolutional Network
Stacked Auto-encoders
11) Algorithm for reducing dimensions:
Principal Component Analysis (PCA)
Partial Least Square Regression (PLS)
Sammon mapping
Multi-Dimensional Scaling (MDS)
Projection Tracking (ProjectionPursuit)
12) Integrated algorithm:
Boosting
Bootstrapped Aggregation(Bagging)
AdaBoost
Stacked Generalization, Blending
Gradient Boosting Machine (GBM)
Random Forest
You can build a knowledge map of machine learning for yourself, and strive to master every classic machine learning theory and algorithm, which is briefly summarized as follows:
1) Regression algorithm:
Least Square Method (OrdinaryLeast Square)
Logistic Regression
Stepwise Regression
(reduction method)
MultivariateAdaptive Regression Splines
Locally Estimated Scatterplot Smoothing
2) Instance-based algorithm:
k-Nearest Neighbor (KNN)
Learning Vector Quantization (LVQ)
Self-Organizing Map (SOM)
3) Based on the regularization method:
Ridge Regression L2
Least Absolute Shrinkage and Selection Operator (LASSO) L1
Elastic Net
4) Decision tree learning:
Classification and Regression Tree (CART)
Http://blog.csdn.net/m0_37167788/article/details/78794833
ID3 (Iterative Dichotomiser 3)
C4.5
Chi-squared Automatic Interaction Detection (CHAID)
Decision Stump
Random Forest
Multivariate Adaptive Regression Spline (MARS)
Gradient Boosting Machine (GBM)
5) Based on Bayesian method:
Naive Bayesian algorithm
Averaged One-Dependence Estimators (AODE)
Bayesian Belief Network (BBN)
6) Kernel-based algorithm:
Support Vector Machine (SVM)
Radial Basis Function (RBF)
Linear Discriminate Analysis (LDA)
7) Clustering algorithm:
k-Means algorithm
Expectation Maximization (EM)
8) Learning based on association rules:
Apriori algorithm
Eclat algorithm
9) Artificial neural network:
Perceptron Neural Network
Back Propagation
Hopfield Network
Self-Organizing Map (SOM)
Learning Vector Quantization (LVQ);
10) Deep learning:
Restricted Boltzmann Machine (RBN)
Deep Belief Networks (DBN)
Convolutional Network
Stacked Auto-encoders
11) Algorithm for reducing dimensions:
Principal Component Analysis (PCA)
Partial Least Square Regression (PLS)
Sammon mapping
Multi-Dimensional Scaling (MDS)
Projection Tracking (ProjectionPursuit)
12) Integrated algorithm:
Boosting
Bootstrapped Aggregation(Bagging)
AdaBoost
Stacked Generalization, Blending
Gradient Boosting Machine (GBM)
Random Forest