半监督学习研究进展

[1] Audio-visual Human Recognition using Semi-supervised Spectral Learning and Hid-den Markov Models. FENG W,XIE L,ZENGJ,LI UZ Q. Journal of Visualization . 2009

[2] Learning with Local and Global Consistency. ZHOU D Y,,BOUSQUET O,LAL T N,et al. Advances in Neural Information Processing System16 . 2004

[3] Semi-supervised Classification by Low Density Separation. CHAPELLE O,ZIEN A. Proceedings ofthe10thInter-national Workshop on Artificial Intelligence and Statistics . 2005

[4] Learning from Labeled and Unlabeled Data using Graph Mincuts. BLUM A,CHAWLA S. Proceedings ofthe18th International Conference on Machine Learning . 2001

[5] Semi-supervised Learning on Directed Graphs. HOU D Y,SCHOLKOPF B,HOFMANN T. Advancesin Neural In-formation Processing System . 2005

[6] Enhancing Supervised Learning with Unlabeled Data. GOLDMAN.S,,ZHOU Y. Proceedings of17th International Conference on Machine Learning . 2000

[7] Semi-supervised Regression with Co-training. ZHOU Z H,LI M. Proceedings ofthe19thInternational Joint Con-ference on Artificial Intelligence . 2005

[8] A Global Opti mization Method for Semi-supervised Clustering. XIA Y. Data Mining and Knowledge Discovery . 2009

[9] Tri-Training:Exploiting Unlabeled Data using Three Classifiers. ZHOU Z H,LI M. IEEE Transactions on Knowl-edge and Data Engineering . 2005

[10] Network traffic classification based on ensemble learning and co-training[J]. HE HaiTao1,2,3,LUO XiaoNan1,2,MA FeiTeng1,CHE ChunHui1 & WANG JianMin1,2 1 School of Information Science and Technology,Sun Yat-Sen University,Guangzhou 510275,China; 2 Key Laboratory of Digital Life (Sun Yat-sen University),Ministry of Education,Guangzhou 510275,China; 3 Information and Network Center,Sun Yat-Sen University,Guangzhou 510275,China.  Science in China(Series F:Information Sciences). 2009(02)

[1] Audio-visual Human Recognition using Semi-supervised Spectral Learning and Hid-den Markov Models. FENG W,XIE L,ZENGJ,LI UZ Q. Journal of Visualization . 2009

[2] Learning with Local and Global Consistency. ZHOU D Y,,BOUSQUET O,LAL T N,et al. Advances in Neural Information Processing System16 . 2004

[3] Semi-supervised Classification by Low Density Separation. CHAPELLE O,ZIEN A. Proceedings ofthe10thInter-national Workshop on Artificial Intelligence and Statistics . 2005

[4] Learning from Labeled and Unlabeled Data using Graph Mincuts. BLUM A,CHAWLA S. Proceedings ofthe18th International Conference on Machine Learning . 2001

[5] Semi-supervised Learning on Directed Graphs. HOU D Y,SCHOLKOPF B,HOFMANN T. Advancesin Neural In-formation Processing System . 2005

[6] Enhancing Supervised Learning with Unlabeled Data. GOLDMAN.S,,ZHOU Y. Proceedings of17th International Conference on Machine Learning . 2000

[7] Semi-supervised Regression with Co-training. ZHOU Z H,LI M. Proceedings ofthe19thInternational Joint Con-ference on Artificial Intelligence . 2005

[8] A Global Opti mization Method for Semi-supervised Clustering. XIA Y. Data Mining and Knowledge Discovery . 2009

[9] Tri-Training:Exploiting Unlabeled Data using Three Classifiers. ZHOU Z H,LI M. IEEE Transactions on Knowl-edge and Data Engineering . 2005

[10] Network traffic classification based on ensemble learning and co-training[J]. HE HaiTao1,2,3,LUO XiaoNan1,2,MA FeiTeng1,CHE ChunHui1 & WANG JianMin1,2 1 School of Information Science and Technology,Sun Yat-Sen University,Guangzhou 510275,China; 2 Key Laboratory of Digital Life (Sun Yat-sen University),Ministry of Education,Guangzhou 510275,China; 3 Information and Network Center,Sun Yat-Sen University,Guangzhou 510275,China.  Science in China(Series F:Information Sciences). 2009(02)