Yang Y M, Liu X. A re-examination of text categorization methods[C]//Proceedings of the 22nd Annual International ACM SIGIR conference on Research and Development in Information Retrieval. New York, USA: Association for Computing Machinery, 1999: 42-49.
[2]
Li B L, Chen Y Z, Yu S W. A comparative study on automatic categorization methods for Chinese search engine[C]//Proceedings of the 8th Joint International Computer Conference. Hangzhou: Zhejiang University Press, 2002:117-120.
[3]
Cover T M, Hart P E. Nearest neighbor pattern classification[J]. IEEE Transactions on Information Theory, 1967, 13(1): 21-27.
[4]
Vivencio D P, Hruschka E R, Nicoletti M C, et al. Feature-weighted k-nearest neighbor classifier[C]//The IEEE Symposium on Foundations of Computational Intelligence.Washington DC, USA: IEEE Communications Society, 2007: 481-486.
[5]
Sun Y,LüS P,Tang Y Y. No previous ordering for KNN algorithm[J]. Journal of Chinese Computer Systems,2008,29(4):682-686.[孙岩,吕世聘,唐-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]
[6]
Duda R O,Hart P E,Stork D G. Pattern Classification[M]. Beijing: China Machine Press, 2006:150-151.[Duda R O,Hart P E,Stork D G. 模式分类[M]. 李宏东, 姚天翔,等译.北京:机械工业出版社, 2006:150-151.
[7]
Gora G, Wojna A. A classifier combining rule induction and KNN method with automated selection of optimal neighbourhood[C]//Proceedings of the 13rd European Conference on Machine Learning. London, UK: Springer-Verlag, 2002:111-123.
[8]
Hechenbichler K, Schliep K. Weighted k-nearest-neighbor techniques and ordinal classification, Discussion Paper 399. Munich, Germany: Ludwig-Maximilians University Munich, 2004.
[9]
Wang B, Yong Z, Yupu Y. Generalized nearest neighbor rule for pattern classification[C]//Proceedings of the 7th World Congress on Intelligent Control and Automation. Washington DC, USA: Institute of Electrical and Electronics Engineers, 2008: 8465-8470.
[10]
Chen Z Z, Li L, Yao Z A. 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.
[11]
Liu M, Yuan B Z, Tang X F. 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]
[12]
Seung H S, Lee D D. The manifold ways of perception[J]. Science,2000,290(5500):2268-2269.
[13]
Zhu M H, Luo D Y, Yi L Q, et al. Incremental locally linear embedding algorithm based on orthogonal iteration method[J]. Acta Electronica Sinica, 2009, 37(1):132-136.[朱明旱, 罗大庸, 易励群,等. 基于正交迭代的增量LLE算法[J].电子学报, 2009, 37(1):132-136]
[14]
Kanade T, Cohn J, Tian Y. Comprehensive database for facial expression analysis[C]//Proceeding of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition. Washington DC, USA:IEEE Computer Society, 2000: 46-53.
[15]
Chen T. CMU-AMP face expression database[EB/OL]. (2002-4-18)[2010-3-12]. http://amp. ece.cmu.edu/projects/Face Authentication/.
[16]
Kong H, Teoh E K, Wang J G. Two dimensional fisher discriminant analysis: forget about small sample size problem[C]//Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing. Washington DC, USA: IEEE Communications Society, 2005:761-764.
[17]
Hei X F, Niyogi P. Locality preserving projections[C]//Proceedings of Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2004:1-8.
[18]
Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290:2323-2326.
[19]
Shan C, Gong S,McOwan P W. Dynamic facial expresson recognition using a Bayesian temporal manifold mode[C]//Proceedings of British Machine Vision Conference. Edinburgh, UK: British Machine Vision Association, 2006: 297-306.
