A Tour of Machine Learning Algorithms-优快云博客

本文链接：https://blog.youkuaiyun.com/itodayer/article/details/52584174

本文介绍了机器学习算法的不同分类方式，包括按学习风格分为有监督学习、无监督学习和半监督学习；按相似性分为回归算法、实例算法等。此外还列举了大量具体的算法示例，如逻辑回归、k-均值聚类等。

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                    Algorithms Grouped by Learning Style

Supervised Learning 

Example problems are classification and regression.
Example algorithms include Logistic Regression and the Back Propagation Neural Network.
Unsupervised Learning 

Example problems are clustering, dimensionality reduction and association rule learning.
Example algorithms include: the Apriori algorithm and k-Means.
Semi-Supervised Learning 

Example problems are classification and regression.
Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabelled data.




Algorithms Grouped By Similarity

Regression Algorithms 

Ordinary Least Squares Regression (OLSR)
Linear Regression
Logistic Regression
Stepwise Regression
Multivariate Adaptive Regression Splines (MARS)
Locally Estimated Scatterplot Smoothing (LOESS)
Instance-based Algorithms 

k-Nearest Neighbour (kNN)
Learning Vector Quantization (LVQ)
Self-Organizing Map (SOM)
Locally Weighted Learning (LWL)
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 (different versions of a powerful approach)
Chi-squared Automatic Interaction Detection (CHAID)
Decision Stump
M5
Conditional Decision Trees
Bayesian Algorithms 

Naive Bayes
Gaussian Naive Bayes
Multinomial Naive Bayes
Averaged One-Dependence Estimators (AODE)
Bayesian Belief Network (BBN)
Bayesian Network (BN)
Clustering Algorithms 

k-Means
k-Medians
Expectation Maximisation (EM)
Hierarchical Clustering
Association Rule Learning Algorithms 

Apriori algorithm
Eclat algorithm
Artificial Neural Network Algorithms 

Perceptron
Back-Propagation
Hopfield Network
Radial Basis Function Network (RBFN)
Deep Learning Algorithms 

Deep Boltzmann Machine (DBM)
Deep Belief Networks (DBN)
Convolutional Neural Network (CNN)
Stacked Auto-Encoders
Dimensionality Reduction Algorithms 

Principal Component Analysis (PCA)
Principal Component Regression (PCR)
Partial Least Squares Regression (PLSR)
Sammon Mapping
Multidimensional Scaling (MDS)
Projection Pursuit
Linear Discriminant Analysis (LDA)
Mixture Discriminant Analysis (MDA)
Quadratic Discriminant Analysis (QDA)
Flexible Discriminant Analysis (FDA)
Ensemble Algorithms 

Boosting
Bootstrapped Aggregation (Bagging)
AdaBoost
Stacked Generalization (blending)
Gradient Boosting Machines (GBM)
Gradient Boosted Regression Trees (GBRT)
Random Forest
Other Algorithms 

Feature selection algorithms
Algorithm accuracy evaluation
Performance measures
Computational intelligence (evolutionary algorithms, etc.)
Computer Vision (CV)
Natural Language Processing (NLP)
Recommender Systems
Reinforcement Learning
Graphical Models
And more…

                