Catalog
- 场景归类
- 常见ML模型
场景归类
- Supervised Learning（监督学习）
- Unsupervised Learning（无监督学习）
- Semi-supervised Learning（半监督学习）
- Reinforcement Learning（强化学习）
Supervised Learning
Definition: Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples.(By Wikipedia)
Characteristic: 给定样例输入输出对
Unsupervised Learning
Definition: Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. (By MathWorks)
Characteristic: 只提供了输入而没有标签作为回应
Semi-Supervised Learning
Semi-supervised learning is a class of machine learning tasks and techniques that also make use of unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data. Semi-supervised learning falls between unsupervised learning and supervised learning.(By Wikipedia)
Characteristic: 提供了一小部分的带有输出的样例对，以及大量的不含有输出标签的数据对，介于SL与USL之间。
Reinforcement Learning
Definition: Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. (By Wikipedia)
Characteristic:允许通过自己的行为作为反馈来适应外界改变。
Analysis
Unlike supervised learning where feedback provided to the agent is correct set of actions for performing a task, reinforcement learning uses rewards and punishment as signals for positive and negative behavior.
强化学习使用奖惩作为代表积极与消极行为的信号，监督学习则使用确定的动作集
While the goal in unsupervised learning is to find similarities and differences between data points, in reinforcement learning the goal is to find a suitable action model that would maximize the total cumulative reward of the agent.
非监督学习的目的是找到数据间潜在的关系，而强化学习则是找到合适的行为模型来最大化累计报酬
常见的应用场景
监督学习：根据特征（身高体重等）进行性别分类，垃圾邮件分类，以及数量回归预测；
常用模型或算法：KNN， SVM，人工神经网络，决策树。
非监督学习： 挖掘数据间的内联关系及结构，“啤酒与纸尿裤”；
常用模型或算法：K-means，Apriori关联规则挖掘
半监督学习：应用于人工标签开销过大的分类及回归。
常用模型或算法：S3VM，S4VM
强化学习：需要通过一系列动作之后才能获得最终结果的训练模型。
常用模型或算法：蒙特卡洛强化学习
