Types of Machine Learning Systems

1.Supervised/Unsupervised Learning

//Whether or not they are trained with human supervision

 

(1)supervised learning  

 

(2)unsupervised learning  

(3)semisupervised learning  

(4)Reinforcement Learning

 

(1)supervised learning

  In supervised learning, the training data you feed to the algorithm includes the desired
solutions, called labels .


A typical supervised learning task is classification.Another typical task is to predict a target numeric value
 

(2)unsupervised learning  

the training data is unlabeled, The system tries to learn without a teacher. 

the following are important unsupervised learning

• Clustering

—k-Means
—Hierarchical Cluster Analysis (HCA)
—Expectation Maximization
• Visualization and dimensionality reduction
—Principal Component Analysis (PCA)
—Kernel PCA
—Locally-Linear Embedding (LLE)
—t-distributed Stochastic Neighbor Embedding (t-SNE)
• Association rule learning

—Apriori

—Eclat

notice :dimensionality reduction 

note:if there is an new instance far away other normal instances ,maybe it's anomaly.

association rule learning.

(3)semisupervised learning  

  Some algorithms can deal with partially labeled training data, usually a lot of unlabeled
data and a little bit of labeled data. This is called semisupervised learning.

(4)Reinforcement Learning

The learning system, called an agent in this context, can observe the environment, select and perform actions, and get rewards in return. 在不断的训练获得回报或惩罚中学习。

 

2.Batch and Online Learning

Batch learning 

系统训练好后下载到实际的生产应用当中运行，在运行中不再学习。如果想要处理新的数据需要在新老数据中重新训练新的版本。

Online learning 

 

3.Instance-Based Versus Model-Based Learning

    One more way to categorize Machine Learning systems is by how they generalize .

(1).Instance-based learning

学习系统牢记已经有过的训练集的样子，然后用相似性计算的方式对新的数据进行归纳。

(2).Model-based learning