Machine Learning and Data Mining
Machine Learning and Data Mining
复制标题
DOI：
10.1007/978-0-85729-299-5_8
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
W. Ertel
中科院分区：
文献类型：
--
作者：
W. Ertel
人工智能的主要应用之一是智能自主机器人的开发。由于灵活性和自适应性是真正智能代理的重要特征，因此学习机制的研究和机器学习算法的开发是人工智能最重要的分支之一。在介绍了机器学习的分类、近似等基本概念之后，本章介绍了感知器、最近邻方法和决策树归纳等基本的监督学习算法。无监督的聚类方法和数据挖掘软件工具完善了这一迷人领域的图景。
One of the major AI applications is the development of intelligent autonomous robots. Since flexibility and adaptivity are important features of really intelligent agents, research into learning mechanisms and the development of machine learning algorithms is one of the most important branches of AI. After motivating and introducing basic concepts of machine learning like classification and approximation, this chapter presents basic supervised learning algorithms such as the perceptron, nearest neighbor methods, and decision tree induction. Unsupervised clustering methods and data mining software tools complete the picture of this fascinating field.