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