统计学习中感知机模型的教学知识点总结分析  

Analysis for Teaching Knowledge Points of Perceptron Model in Statistic Learning

作　　者：肖立顺 徐娜 赵华硕 金英良 XIAO Li-shun;XU Na;ZHAO Hua-shuo;JIN Ying-liang(School of Public Health,Xuzhou Medical University,Xuzhou,Jiangsu 221004,China;Shude Middle School of Xuzhou,Xuzhou,Jiangsu 221007,China)

机构地区：[1]徐州医科大学公共卫生学院流行病与卫生统计学教研室,江苏徐州221004 [2]徐州树德中学,江苏徐州221007

出　　处：《教育教学论坛》2019年第36期205-206,共2页Education And Teaching Forum

基　　金：徐州医科大学优秀人才科研启动基金（D2018002）

摘　　要：感知机是统计学习和机器学习中经典的线性二分类模型。实际问题中特征空间的维数较大,感知机模型并不能直观地展现出来,所以在教学过程中一般以二维形式进行讲解。文章针对二维平面中感知机模型的几何解释,总结了其教学过程中应重点讲解的三个知识点,并对这些知识点进行了详细分析。In machine learning and statistic learning,perceptron is a classical linear binary classifier model.Since the dimension numbers of feature space is very large in practical applications,perceptron cannot be intuitively illustrated.So during the learning process perceptron is interpreted in two dimensions.For perceptron in two-dimension plane,this paper summarizes and provides detailed analysis for three knowledge points which should be emphasized in learning process.

关 键 词：感知机 二分类 知识点分析 

分 类 号：G642.0[文化科学—高等教育学]