Cover T M,Hart P E.Nearest neighbor pattern classification[J].IEEE Transactions on Information Theory,1967,13(1): 21-27.
[2]
Edward A P,Frederick P F.A generalized k-nearest neighbor rule [J].Information and Control,1970,16(2):128-152.
[3]
Hechenbichler K,Schliep K.Weighted k-nearest neighbor techniques and ordinal classification:discussion paper 399[R].Munich,Germany: Ludwig-Maximilians University,2004.
[4]
Chen Zhenzhou,Li Lei,Yao Zheng\'an.Feature-weighted k-nearest neighbor algorithm with SVM [J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2005,44(1):17-20.[陈振洲,李磊,姚正安.基于SVM的特征加权kNN算法[J].中山大学学报:自然科学版,2005,44(1):17-20.]
[5]
Liu Ming,Yuan Baozong,Tang Xiaofang.A new approach to determine the similarity parameters in evidence-theoretic k-NN rule [J].Acta Electronica Sinica,2005,33(4):766-768.[刘明,袁保宗,唐晓芳.证据理论k-NN规则中确定相似度参数的新方法[J].电子学报,2005,33(4):766-768.]
[6]
Vivencio D P,Hruschka E R,Nicoletti M C,et al.Feature-weighted k-nearest neighbor classifier [C]//Proceedings of the IEEE Symposium on Foundations of Computational Intelligence.Washington DC,USA: IEEE Communications Society,2007,481-486.
[7]
Sun Yan,Lv Shipin,Tang Yiyuan.No previous ordering for kNN algorithm[J].Journal of Chinese Computer Systems,2008,29(4): 682-686.[孙岩,吕世聘,唐一源.无先序条件约束的kNN算法[J].小型微型计算机系统,2008,29(4):682-686.]
Duda R O,Hart P E,Stork D G.Pattern Classification[M].2 ed.Beijing:China Machine Prees,2003,146-151.[Duda R O,Hart P E,Stork D G.模式分类[M].2版.李宏东,姚天翔译.北京: 机械工业出版社,2003,146-151.]
[10]
Joachims T.Transductive inference for text classification using support vector machines[C]//Proceedings of the 16th International Conference on Machine Learning.San Francisco: Morgan Kaufmann Publishers,1999,200-209.
[11]
Bennett K P,Demiriz A.Semi-supervised support vector machines[C]//Proceedings of the 1998 Conference on Advances in Neural Information Processing Systems.Cambridge,MA: MIT Press,1999,368-374.
[12]
Chapelle O,Zien A.Semi-supervised classification by low density separation[C]// Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics.New Jersey: The Society for Artificial Intelligence and Statistics,2005,57-64.
[13]
Chapelle O,Weston J,Sch?lkopf B.Cluster kernels for semi-supervised learning[C]//Proceedings of the 2003 Conference on Advances in Neural Information Processing Systems 15.Cambridge,MA: MIT Press,2003,585-592.
[14]
Balaji K,David W,Ya X,et al.On semi-supervised classification[C]//Proceedings of Neural Information Processing Systems Conferences,Vancouver,Canada:British Columbia,2005,721-728.
[15]
Belkin M,Matveeva I,Niyogi P.Regularization and semi-supervised learning on large graphs[C]// Proceedings 17th Annual Conference on Learning Theory.Berlin,Heidelberg,Germany: Springer Press,2004,624-638.
[16]
Li Wei, Eamonn Keogh.Semi-supervised time series classification[C]//Proceedings of the 12th ACM SIGKDD International,ACM.New York,NY,USA,2006,748-753.
[17]
Zhu Xiaojin,Timothy Rogers,Ruichen Qian,et al.Humans perform semi-supervised classification too[C]// Proceedings of the 22nd National Conference on Artificial Intelligence.Menlo Park,Calif: AAAI Press,2007,864-869.
[18]
Yann Lecun,Corinna Cortes.The MNIST Database of Handwritten Digits [EB/OL].[2010-04-15].http://yann.lecun.com/exdb/mnist/.
[19]
AT & T Laboratories Cambridge.The ORL Database of Faces [EB/OL].[2010-04-15].http://www.cl.cam.ac.uk/Research/ DTG/attarchive/ facedatabase.html/.
[20]
Yang J,Zhang D.Two-dimensional PCA: a new approach to appearance-based face representation and recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(l): 131-137.