参考文献/References:
[1]EISEN M B, SPELLMAN P T, BROWN P O, et al. Cluster analysis and dis p lay of genomewide expression patterns[C]// Proceedings of the National Acad em y of Science of the United States of America. Washington,D.C,USA, 1998.
[2]TAMAYO P, SLONIM D, MESIROV J, et al. Interpreting patterns of gene expres sion with selforganizing maps[C]// Proceedings of the National Academy of S ciences of the United States of America. Washington,D.C,USA, 1999.
[3]WU S, LIEW A W C, YAN H, et al. Cluster analysis of gene expression data b ased on selfsplitting and merging competitive learning[J]. IEEE Transactions on Information Technology in Biomedicine, 2004, 8(1):515.
[4]MCCALLUM A K. Multilabel text classification with a mixture model trained by EM[C]// Working Notes of the AAAI’99 Workshop on Text Learning. Orl ando,USA,1999.
[5]SCHAPIRE R E, SINGER Y. Boostexter: a boostingbased system for text categ orization[J]. Machine Learning, 2000, 39(23):135168.
[6]ELISSEEFF A, WESTON J. A kernel method for multilabeled classification[C ]// Advances in Neural Information Processing Systems 14. Cambridge: MI T Press,2002.
[7]BOUTELL M R, LUO J, SHEN X, et al. Learning multilabel scene classifica tion[J]. Pattern Recognition, 2004, 37(9): 17571771.
[8]OGIHARA LI T M. Detecting emotion in music[C]// Proceedings of the Inter national Symposium on Music Information Retrieval. Maryland, USA: ISMIR Pre ss,2003.
[9]ZHU X J. Semisupervised learning literature survey[R]. Department of Computer Sciences, University of Wisconsin, Madison, 2005.
[10]ZHANG M L, ZHOU Z H. MLKNN: a lazy learning approach to multil abel lea rning[J]. Pattern Recognition, 2007, 40(7): 20382048.
[11]TSOUMAKAS G, KATAKIS I. Multilabel classification: an overview[J]. Int ernational Journal of Data Warehousing and Mining, 2007, 3(3):113.
[12]CLARE A, KING R D. Knowledge discovery in multilabel phenotype data[C] // Proceedings of the 5th European Conference on Principles of Data Mining and Kn owledge Discovery (PKDD 2001). Freiberg, Germany: Springer, 2001.
[13]LUO X, ZINCIR H. Evaluation of two systems on multiclass multilabel do cument classification[C]// Lecture Notes in Computer Science. Freiberg,Germany : Springer,2005.
[14]GODBOLE S, SARAWAGI S. Discriminative methods for multilabeled classific ation[C]// Lecture Notes in Computer Science. Germany: Springer,2004.
[15]ZHOU Z H, ZHANG M L. Multiinstance multilabel learning with applicat ion to scene classification[C]// Advances in Neural Information Processing Sy stems.Cambridge: MIT Press,2007.
[16]ZHANG M L, ZHOU Z H. Multilabel neural networks with applications to func tional genomics and text categorization[J]. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(10): 13381351.
[17]施彤年, 卢忠良, 荣 融,等.多类多标签汉语文本自动分类的研究[J].情报学报, 2003, 22(3): 306309.
SHI Tongnian, LU Zhongliang, RONG Rong ,et al. Research on the Chinese text c ategorization of multiclassification and multilabel[J]. Jou rnal of the China Society for Scientific and Technical Information, 2003, 22(3): 306309.
[18]LIU Y, JIN R, LIU Y. Semisupervised multilabel learning by cons trained nonnegative matrix factorization[C]// Proceeding of the TwentyFir st National Conference on Artificial Intelligence, Eighteenth Conference on Innova tive Applications of Artificial Intelligence. Boston: AAAI Press, 2006.
[19]宫秀军, 史忠植. 基于Bayes潜在语义模型的半监督Web挖掘[J]. 软件学报, 2002, 12(8):15081514.
GONG Xiujun, SHI Zhongzhi. Semisupervised web mining based on bayes late nt sem antic model[J]. Journal of Software, 2002, 12(8): 15081514.
[20]彭 雅, 林亚平, 陈治平. TFIDF_NB协同训练算法[J]. 小型微型计算机, 2004, 2 5(12): 22432246.
PENG Ya, LIN Yaping, CHEN Zhiping. TFIDFNB cooperative train ing algorithm[J]. Minimicro Systems, 2004, 25(12): 22432246.
[21]KLAUS B, JOHANNS F, EYKE H. A unified model for multilabel classifi cation and ranking[C]// Proceeding of the 15th Eureopean Conference on Artifi ci al Intelligence. Riva del Garda, Italy: IOS Press, 2006.
[22]PAVLIDIS P, WESTON J, CAI J, et al. Combining microarray expressio n data and phylogenetic protellles to learn functional categories using support vector machines[R].CUCS011000, Department of Computer Sc ience , Columbia University, Columbia, 2000.
[23]DIPLARIS S, TSOUMAKAS G, MITKAS P, et al. Protein classification w ith multiple algorithms[C]// Lecture Notes in Computer Science.Volo s, Greece: Springer, 2005.
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