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MindMap Gallery Machine learning algorithm classification

Machine learning algorithm classification

Summary of classification of machine learning algorithms! The figure below summarizes the classification of machine learning algorithms, including supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, transfer learning, deep learning algorithms, etc. Hope this picture can help you!

Edited at 2020-04-08 10:06:19

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Machine learning algorithm classification

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Machine learning algorithm classification

Machine learning is classified according to training methods

Supervised Learning

Artificial Neural Network class

Backpropagation

Boltzmann Machine

Convolutional Neural Network

Hopfield Network

Multilayer Perceptron

Radial Basis Function Network (RBFN)

Restricted Boltzmann Machine

Recurrent Neural Network (RNN)

Self-organizing Map (SOM)

Spiking Neural Network

Bayesin

Naive Bayes

Gaussian Naive Bayes

Multinomial Naive Bayes

Averaged One-Dependence Estimators (AODE)

Bayesian Belief Network (BBN)

Bayesian Network (BN)

Decision Tree class

Classification and Regression Tree (CART)

Iterative Dichotomiser3 (Iterative Dichotomiser 3, ID3)

C4.5 Algorithm

C5.0 Algorithm (C5.0 Algorithm)

Chi-squared Automatic Interaction Detection (CHAID)

Decision Stump

ID3 Algorithm

Random Forest

SLIQ (Supervised Learning in Quest)

Linear Classifier class

Fisher’s Linear Discriminant

Linear Regression

Logistic Regression

Multinomial Logistic Regression

Naive Bayes Classifier

Perception

Support Vector Machine

Unsupervised Learning

Artificial Neural Network class

Generative Adversarial Networks (GAN)

Feedforward Neural Network

Logic Learning Machine

Self-organizing Map

Association Rule Learning class

Apriori Algorithm

Eclat Algorithm

FP-Growth algorithm

Hierarchical Clustering

Single-linkage Clustering

Conceptual Clustering

Cluster analysis

BIRCH algorithm

DBSCAN algorithm

Expectation-maximization (EM)

Fuzzy Clustering

K-means algorithm

K-means Clustering

K-medians clustering

Mean-shift algorithm (Mean-shift)

OPTICS algorithm

Anomaly detection class

K-nearest Neighbor (KNN) algorithm

Local Outlier Factor (LOF) algorithm

Semi-supervised Learning

Generative Models

Low-density Separation

Graph-based Methods

Co-training

Reinforcement Learning

Q-learning

State-Action-Reward-State-Action (State-Action-Reward-State-Action, SARSA)

DQN (Deep Q Network)

Policy Gradients

Model Based RL

Temporal Differential Learning

Deep Learning

Deep Belief Machines

Deep Convolutional Neural Networks

Deep Recurrent Neural Network

Hierarchical Temporal Memory (HTM)

Deep Boltzmann Machine (DBM)

Stacked Autoencoder

Generative Adversarial Networks

Transfer Learning

Inductive Transfer Learning

Transductive Transfer Learning

Unsupervised Transfer Learning

Transitive Transfer Learning

Machine learning is classified according to problem solving

Two-class Classification

Two-class SVM: suitable for scenarios with many data features and linear models

Two-class Average Perceptron: Suitable for scenarios with short training time and linear models.

Two-class Logistic Regression: Suitable for scenarios with short training time and linear models

Two-class Bayes Point Machine: suitable for scenarios with short training time and linear models

Two-class Decision Forest: suitable for scenarios with short training time and accuracy

Two-class Boosted Decision Tree: suitable for scenarios with short training time, high accuracy, and large memory usage

Two-class Decision Jungle: suitable for scenarios with short training time, high accuracy, and small memory footprint

Two-class Locally Deep SVM: suitable for scenarios with many data features

Two-class Neural Network: suitable for scenarios with high accuracy and long training time

Multi-class Classification

Multiclass Logistic Regression: Suitable for scenarios with short training time and linear models

Multiclass Neural Network: suitable for scenarios with high accuracy and long training time

Multiclass Decision Forest: suitable for scenarios with high accuracy and short training time

Multiclass Decision Jungle: suitable for scenarios with high accuracy and small memory footprint

"One-vs-all Multiclass": depends on the effect of the two classifiers

Regression algorithm

Ordinal Regression: Suitable for scenarios where data is classified and sorted

Poisson Regression: Suitable for predicting the number of events

Fast Forest Quantile Regression: Suitable for scenarios of predicting distributions

Linear Regression: Suitable for scenarios with short training time and linear models

Bayesian Linear Regression: suitable for linear models and scenarios where the amount of training data is small

Neural Network Regression: suitable for scenarios with high accuracy and long training time

Decision Forest Regression: suitable for scenarios with high accuracy and short training time

Boosted Decision Tree Regression: suitable for scenarios with high accuracy, short training time, and large memory usage

Clustering algorithm

Hierarchical Clustering: suitable for scenarios with short training time and large amount of data

K-means algorithm: suitable for scenarios with high accuracy and short training time

Fuzzy clustering FCM algorithm (Fuzzy C-means, FCM): suitable for scenarios with high accuracy and short training time

SOM neural network (Self-organizing Feature Map, SOM): suitable for scenarios with long running time

Anomaly Detection

One-class support vector machine (One-class SVM): suitable for scenarios with many data features

PCA-based Anomaly Detection: suitable for scenarios with short training time