Overview of common algorithms for machine learning

This paper mainly includes the realization of common machine learning algorithms, in which the mathematical derivation, principle and parallel implementation will give the link.

Machine Learning (machines learning, ML) is a multidisciplinary interdisciplinary subject involving probability theory, statistics, approximation theory, convex analysis, algorithmic complexity theory and many other disciplines. Specialized in computer simulation or realization of human learning behavior, in order to acquire new knowledge or skills, reorganize the existing knowledge structure to continuously improve their performance. It is the core of artificial intelligence, is the fundamental way to make the computer intelligent, its application throughout the field of artificial intelligence, it mainly uses induction, synthesis rather than deduction.

1. K Nearest neighbor (KNN)

Algorithm

KNN algorithm for approximation of discrete-valued function f

To modify the target function, you can approximate the target function of the continuous value

You can use distance weighting

which

Realize

(1)C + + version http://blog.csdn.net/mimi9919/article/details/51172095
(2)Python version can refer to machine learning combat

2. Perception Machine

Algorithm

Realize

(1)C + + version http://blog.csdn.net/idmer/article/details/49365301

3. Naive Bayes

Algorithm

Realize

(1)C + + version http://blog.csdn.net/idmer/article/details/48809677

4. Categorical regression tree (CART)

The cart evolved slowly from id3,c4.5, which is the foundation of many tree-based bagging and boosting models, and is very important.

Algorithm

Of these, 5.25 are as follows

Realize

(1)C + + version http://blog.csdn.net/a819825294/article/details/51995323
(2)Python version can refer to machine learning combat

5. Logistic regression (LR)

Algorithm

(1) Model parameter estimation

(2) Gradient descent learning parameters

(3) Final model

More principles

http://blog.csdn.net/a819825294/article/details/51172466

Realize

(1)C + + version http://www.chawenti.com/articles/15254.html
(2)Python version can refer to machine learning combat

Distributed

Http://www.csdn.net/article/2014-02-13/2818400-2014-02-13

6. Support Vector Machine (SVM)

Algorithm

More principles

http://blog.csdn.net/a819825294/article/details/51679152

Realize

(1)C + + version LIBSVM
(2)Python version can refer to machine learning combat

7. Neural Network (NN)

Algorithm

Realize

(1)C + + version https://github.com/matthewrdev/Neural-Network

8. Random Forest (RF)

Algorithm

More principles

http://blog.csdn.net/a819825294/article/details/51177435

Realize

(1)C + + version http://download.csdn.net/download/qq_17506541/8866653

9, AdaBoost

Algorithm

Realize

(1)C + + version http://blog.csdn.net/a819825294/article/details/51995323
(2)Python version can refer to machine learning combat

10. Gradient Lifting Tree (GBDT)

Algorithm

More principles

http://blog.csdn.net/a819825294/article/details/51188740

Realize

(1)C + + version http://blog.csdn.net/a819825294/article/details/51995323

11, Xgboost

More principles

http://blog.csdn.net/a819825294/article/details/51206410

Realize

Https://github.com/dmlc/xgboost

12, Kmeans

Algorithm

Realize

(1)C + + version http://blog.csdn.net/qll125596718/article/details/8243404

13. PCA

Algorithm

Reference documents
(1) "Machine learning", Carnegie Mellon University, Tom M.mitchell
(2) "Machine learning" Zhou Zhihua
(3) "Statistical learning method" Li Hang

Overview of common algorithms for machine learning