机器学习经典算法及特点：机器学习常用算法

逗爷 2023-03-12 06:47:33 318

机器学习经典算法及特点：机器学习常用算法（1）机器学习是一门人工智能的科学，该领域的主要研究对象是人工智能，特别是如何在经验学习中改善具体算法的性能。机器学习的定义它是人工智能核心，是使计算机具有智能的根本途径。Machine learning is a multidisciplinary discipline involving probability theory statistics approximation theory convex analysis algorithm complexity theory and other disciplines. It specializes in the study of how computers simulate or realize human learning behavior to acquire new knowledge or skills and reo

分享兴趣，传播快乐，增长见闻，留下美好！

少年易老学难成，一寸光阴不可轻。

什么是机器学习

机器学习经典算法及特点：机器学习常用算法(1)

机器学习是一门多领域交叉学科，涉及概率论、统计学、逼近论、凸分析、算法复杂度理论等多门学科。专门研究计算机怎样模拟或实现人类的学习行为，以获取新的知识或技能，重新组织已有的知识结构使之不断改善自身的性能。

它是人工智能核心，是使计算机具有智能的根本途径。

Machine learning is a multidisciplinary discipline involving probability theory statistics approximation theory convex analysis algorithm complexity theory and other disciplines. It specializes in the study of how computers simulate or realize human learning behavior to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve its own performance.

It is the core of artificial intelligence and the fundamental way to make computers intelligent.

机器学习的定义

（1）机器学习是一门人工智能的科学，该领域的主要研究对象是人工智能，特别是如何在经验学习中改善具体算法的性能。

（2）机器学习是对能通过经验自动改进的计算机算法的研究。

（3）机器学习运用数据或以往的经验，以此优化计算机程序的性能标准

Definition of machine learning

(1) Machine learning is a science of artificial intelligence. The main research object of this field is artificial intelligence especially how to improve the performance of specific algorithms in experiential learning.

(2) Machine learning is the study of computer algorithms that can be improved automatically through experience.

(3) Machine learning is the use of data or past experience in order to optimize the performance criteria of computer programs

Logistic回归

Logistic是用来分类的，是一种线性分类器，logsitc回归方法主要是用最大似然估计来学习的，logistic函数表达式为：

机器学习经典算法及特点：机器学习常用算法(2)

Logistic回归优点：

1. 实现简单

2. 分类时计算量非常小，速度很快，存储资源低；

缺点：

1. 容易欠拟合，一般准确度不太高

2. 只能处理两分类问题（在此基础上衍生出来的softmax可以用于多分类），且必须线性可分；

Logistic is used for classification. It is a linear classifier. Logsitc regression method is mainly learned by maximum likelihood estimation.

Advantages of logistic regression:

1 Simple implementation

2 When classifying the amount of calculation is very small the speed is very fast and the storage resources are low;

Disadvantages:

1 It is easy to under fit and the general accuracy is not too high.

2 It can only deal with two classification problems (softmax derived from this can be used for multi classification) and it must be linearly separable;

机器学习经典算法及特点：机器学习常用算法(3)