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自从计算机被发明以来,人们就想知道它能不能学习。机器学习从本质上是一个多学科的领域。它吸取了人工智能、概率统计、计算复杂性理论、控制论、信息论、哲学、生理学、神经生物学等学科的成果。文章主要从统计学习基础的角度对机器学习的发展历程以及一些相关的常用算法进行了简要的回顾和介绍。
Abstract：Since the computer was invented,people have been wanted to know that whether it can learn.Machine learning is essentially a multidisciplinary field. It absorbed some results of artificial intelligence,probability and statistics,computational complexity theory,control theory,information theory,philosophy,physiology,neurobiological.This paper mainly based on statistical learning wanted to give a brief review and presentation to the perspective of machine learning and the development of related algorithms.
[1]龙卫江.基于相近原则的半指导直推学习及其增量算法[J].应用数学学报,2006,29(4):619-632.
[2]Nigam K,McCallum A K,Thrun S,Mitchell T.Text classification from labeled and unlabeled documents using EM[J].Ma-chine Learning,2000,39(2-3):103-134.
[3]Joachims T.Transductive inference for text classification using support vector machines[G].In:Proc 16th Int’l Conf MachineLearning,Bled,Slovenia,1999,200-209.
[4]Blum A,Mitchell T.Combining labeled and unlabeled data with co-training[G].In:Proc 16th Annual Conf ComputationalLearning Theory,Madison,WI,1998,92-100.
[5]刘琴.机器学习[J].武钢职工大学学报,2001(6):41-44.
[6]Breiman L.,Friedman,J.,Olshen,R.,and Stone,C.Classification and Regression Trees[M].Wadsworth,1984.
[7]Breiman L.Random forests[J].Machine Learning,2001,45(1):5-32.
[8]漆书青,戴海琦,丁树良.现代教育与心理测量学原理[M].南昌:江西教育出版社,1998.
[9]Cortes,C.,Vapnik,V.M.,.Support Vector Networks[J].Machine Learning,1995,20:273-297.
[10]Dietterich,T.G.An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees:Bagging,Boosting,and Randomization[J].Machine Learning,2000,40:139-157.
[11]Breiman,L.,Bagging Predictors[J].Machine Learning,1996,24(2):123-140.
[12]Agrawal R,Imielinski T,Swami A.Mining association rules between sets of items in large databases[A][G].Proc of ACMSIGMOD Conf on Management of Data[C].Washington,1993.207-216.
[13]刘星沙,谭利球,等.关联规则挖掘算法及其应用[J].计算机工程与科学,2007,29(1):83-86.
[14]P.Dempster,N.M.Laird,AandD.B.Rubin.Maximum Likelihood From Incomplete Data Via the EM Algorithm[J].RoyalStat.Soc.1997,39(1):1-38.
Basic Information:
DOI：
China Classification Code:TP181
Citation Information:
[1]陈凯,朱钰.机器学习及其相关算法综述[J].统计与信息论坛,2007,No.86(05):105-112.
Fund Information:
国家自然科学基金重点项目(10431010);; 教育部重点基地重大项目(05JJD910001);; 中国人民大学应用统计中心项目