计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2009年
22期
103-106
,共4页
半监督学习%协同训练%Tri-training%支持向量机%最小二乘支持向量机
半鑑督學習%協同訓練%Tri-training%支持嚮量機%最小二乘支持嚮量機
반감독학습%협동훈련%Tri-training%지지향량궤%최소이승지지향량궤
seroi-supervised learning%co-training%Tri-training%Support Vector Maehine(SVM)%least square support vector machine
当前机器学习面临的主要问题之一是如何有效地处理海量数据,而标记训练数据是十分有限且不易获得的.提出了一种新的半监督SVM算法,该算法在对SVM训练中,只要求少量的标记数据,并能利用大量的未标记数据对分类器反复的修正.在实验中发现,Tri-training的应用确实能够提高SVM算法的分类精度,并且通过增大分类器问的差异性能够获得更好的分类效果,所以Tri-raining对分类器的要求十分宽松,通过SVM的不同核函数来体现分类器之间的差异性,进一步改善了协同训练的性能.理论分析与实验表明,该算法具有较好的学习效果.
噹前機器學習麵臨的主要問題之一是如何有效地處理海量數據,而標記訓練數據是十分有限且不易穫得的.提齣瞭一種新的半鑑督SVM算法,該算法在對SVM訓練中,隻要求少量的標記數據,併能利用大量的未標記數據對分類器反複的脩正.在實驗中髮現,Tri-training的應用確實能夠提高SVM算法的分類精度,併且通過增大分類器問的差異性能夠穫得更好的分類效果,所以Tri-raining對分類器的要求十分寬鬆,通過SVM的不同覈函數來體現分類器之間的差異性,進一步改善瞭協同訓練的性能.理論分析與實驗錶明,該算法具有較好的學習效果.
당전궤기학습면림적주요문제지일시여하유효지처리해량수거,이표기훈련수거시십분유한차불역획득적.제출료일충신적반감독SVM산법,해산법재대SVM훈련중,지요구소량적표기수거,병능이용대량적미표기수거대분류기반복적수정.재실험중발현,Tri-training적응용학실능구제고SVM산법적분류정도,병차통과증대분류기문적차이성능구획득경호적분류효과,소이Tri-raining대분류기적요구십분관송,통과SVM적불동핵함수래체현분류기지간적차이성,진일보개선료협동훈련적성능.이론분석여실험표명,해산법구유교호적학습효과.
One of the main difficulties in machine learning is how to solve large-scale problem effectively,and the labeled data are limited and fairly expensive to obtain.In this paper a new semi-supervised SVM is proposed.It applies Tri-training to improve SVM.The semi-supervised SVM uses a few labeled data to train few initial SVM classifiers and makes use of the large number unlabeled data to modify the classifier iteratively.Experiroents on UCI dataset show that Tri-training can improve the classification accuracy of SVM and can increase the difference of classifier,the accuracy of final classifier will be higher.Although Tri-training doesn't put any constraints on the supervised learning algorithm,the proposed method uses the SVMs with three different kernel functions as the supervised learning algorithm.The different kernel can increase the difference of the three SVMs,so the performance of co-training will be better.Theoretical analysis and experiments show that the proposed algorithm has excellentaccuracy and speed of classification.
