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编程日寄 - 机器学习常用算法（21）

科技 07-15 来源： LearningYard学苑

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《编程日寄 | 机器学习常用算法（21）》，

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Today, the editor brings you

"Common Algorithms for Machine Learning (21)"

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什么是机器学习

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

集成学习（Ensemble Learning）

集成学习（Ensemble Learning）通过构建并结合多个基分类器来完成学习任务，有时也被称为多分类器系统（Multi-Classifier System）、基于委员会的学习（Committee-based Learning）等。集成学习通过将多个基分类器进行结合，常常可以获得比单一分类器更为优越的泛化性能。

集成学习在各个规模的数据集上都有很好的策略。

数据集大：划分成多个小数据集，学习多个模型进行组合。

数据集小：利用Bootstrap方法进行抽样，得到多个数据集，分别训练多个模型再进行组合。

Ensemble Learning completes Learning tasks by constructing and combining multiple base classifiers. It is sometimes called multi-classifier System or committee-based Learning. Ensemble learning often achieves better generalization performance than single classifier by combining multiple base classifiers.
Integrated learning has good strategies for data sets of all sizes. Large data set: divide into multiple small data sets and learn multiple models for combination. Small data set: Bootstrap method was used for sampling to obtain multiple data sets, and multiple models were trained and combined respectively.