Machine Learning Algorithms Grouped by Similarity

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Machine Learning Algorithms Grouped by Similarity by Mind Map: Machine Learning Algorithms Grouped by Similarity

1. Instance Based

1.1. kNN

1.2. Learning Vector Quantization (LVQ)

1.3. Self Optimizing Map (SOM)

1.4. Locally Weighted Learning (LWL

2. Hierarchical Clustering

3. Clustering

3.1. k-means

3.2. k-medians

3.3. Expectation Maximization

4. Dimensionality Reduction

4.1. Principal Component Analysis (PCA)

4.2. Partial Least Square Reduction (PLSR)

4.3. Sammon Mapping

4.4. Multi Dimensional Scaling (MDS)

4.5. Projection Pursuit

4.6. Principal Component Regression (PCR)

4.7. Discriminant Analysis

4.7.1. Linear

4.7.2. Regularized

4.7.3. Quadratic

4.7.4. Flexible

4.7.5. Mixture

4.7.6. Partial Least Squared

5. Neural Network

5.1. Radial Basis Function Network (RBFN)

5.2. Perceptron

5.3. Back Propagation

5.4. Hopfield Network

6. Ensemble

6.1. Random Forest

6.2. Gradient Boosting Machines (GBM)

6.3. Boosting

6.4. Bagging (Bootstrapped Aggregation)

6.5. AdaBoost

6.6. Blending (Stacked Generalization)

6.7. Gradient Boosting Regression Trees (GBRT)

7. Regression

7.1. Linear Regression

7.2. Ordinary Least Squared Regression (OLSR)

7.3. Step-wise Regression

7.4. Logistic Regression

7.5. Multivariate Adaptive Regression Splines (MARS)

7.6. Locally Estimated Scatterplot Smoothing (LOESS)

8. Regularization

8.1. Ridge Regression

8.2. Least Absolution Shrinkage & Selection Operator (LASSO)

8.3. Elastic Net

8.4. Least Angle Regression (LARS)

9. Rule System

9.1. Cubist

9.2. One Rule (OneR)

9.3. Zero Rule (ZeroR)

9.4. Repeated Incremental Pruning to Produce Error Reduction (RIPPER)

10. Decision Tree

10.1. Classification & Regression Tree CART

10.2. Iterative Dichotomiser 3 (ID3)

10.3. C 4.5

10.4. C 5.0

10.5. Chi Squared Automatic Interaction Detection (CHAID)

10.6. Decision Stump

10.7. Conditional Decision Tree (CDT)

10.8. M 5

11. Deep Learning

11.1. Deep Boltzmann Machine (DBM)

11.2. Deep Belief Network (DBN)

11.3. Convolutional Neural Networks (CNN)

11.4. Stacked Auto Encoders

12. Bayesian

12.1. Naive Bayes

12.2. Average One Dependence Estimators (AODE)

12.3. Bayesian Belief Network (BBN)

12.4. Bayesian Network (BN)

12.5. Gaussian Naive Bayes

12.6. Multinomial Naive Bayes