[1]CHEN Xiaohua,LIU Dalian,TIAN Yingjie,et al.Extension support vector classification machine[J].CAAI Transactions on Intelligent Systems,2018,13(1):147-151.[doi:10.11992/tis.201610019]
Copy

Extension support vector classification machine

References:
[1] 蔡文, 杨春燕, 陈文伟, 等. 可拓集与可拓数据挖掘[M]. 北京: 科学出版杜, 2008.
CAI Wen, YANG Chunyan, CHEN Wenwei, et al. Extension set and extension data mining[M]. Beijing: Science Press, 2008.
[2] 杨春燕, 蔡文. 可拓工程[M]. 北京: 科学出版社, 2007.
YANG Chunyan, CAI Wen. Extension engineering[M]. Beijing: Science Press, 2007.
[3] CAI Wen. Extension theory and its application[J]. Chinese science bulletin, 1999, 44(17): 1538-1548.
[4] 李立希, 李铧汶, 杨春燕. 可拓学在数据挖掘中的应用初探[J]. 中国工程科学, 2004, 6(7): 53-59.
LI Lixi, LI Huawen, YANG Chunyan. Study on the application of extenics in data mining[J]. Engineering science, 2004, 6(7): 53-59.
[5] 陈文伟, 黄金才. 从数据挖掘到可拓数据挖掘[J]. 智能技术, 2006, 1(2): 50-52.
CHEN Wenwei, HUANG Jincai. From data mining to extension data mining[J]. Intelligent technology, 2006, 1(2): 50-52.
[6] 杨春燕, 蔡文. 可拓数据挖掘研究进展[J]. 数学的实践与认识, 2009, 39(4): 136-141.
YANG Chunyan, CAI Wen. Recent progress in extension data mining[J]. Mathematics in practice and theory, 2009, 39(4): 136-141.
[7] 蔡文, 杨春燕, 何斌. 可拓逻辑初步[M]. 北京: 科学出版社, 2003.
CAI Wen, YANG Chunyan, HE Bin. Extension logic[M]. Beijing: Science Press, 2003.
[8] 蔡文, 杨春燕, 林伟初. 可拓工程方法[M]. 北京: 科学出版社, 1999.
CAI Wen, YANG Chunyan, LIN Weichu. Extension engineering methods[M]. Beijing: Science Press, 1999.
[9] CORTES C, VAPNIK V. Support-vector networks[J]. Machine learning, 1995, 20(3): 273-297.
[10] CRISTIANINI N, SHAWE-TAYLOR J. An introduction to support vector machines and other kernel-based learning methods[M]. Cambridge: Cambridge University Press, 2000.
[11] VAPNIK V N. Statistical learning theory[M]. New York: John Wiley and Sons, 1998.
[12] NOBLE W S. Support vector machine applications in computational biology[M]//SCH?ELKOPF B, TSUDA K, VERT J P. Kernel Methods in Computational Biology. Cambridge: MIT Press, 2004.
[13] ADANKON M M, CHERIET M. Model selection for the LS-SVM. application to handwriting recognition[J]. Pattern recognition, 2009, 42(12): 3264-3270.
[14] TIAN Yingjie, SHI Yong, LIU Xiaohui. Recent advances on support vector machines research[J]. Technological and economic development of economy, 2012, 18(1): 5-33.
[15] DENG Naiyang, TIAN Yingjie, ZHANG Chunhua. Support vector machines: optimization based theory, algorithms, and extensions[M]. Boca Raton: CRC Press, 2013.
[16] HUANG Kaizhu, YANG Haiqin, KING I, et al. Learning large margin classifiers locally and globally[C]//Proceeding of the 21st International Conference on Machine Learning. Banff, Alberta, Canada, 2004.
[17] SHAWE-TAYLOR J, CRISTIANINI N. Kernel methods for pattern analysis[M]. Cambridge: Cambridge University Press, 2004.
[18] BORGWARDT K M. Kernel methods in bioinformatics[M]//LU H H S, SCH?LKOPF B, ZHAO Hongyu. Handbook of Statistical Bioinformatics. Berlin, Heidelberg: Springer, 2011: 317-334.
[19] VAPNIK V, IZMAILOV R. Learning using privileged information: Similarity control and knowledge transfer[J], Journal of machine learning research, 2015, 16: 2023-2049.
[20] SUN S. A survey of multi-view machine learning[J]. Neural computing and applications, 23(7): 2031-2038.
[21] CHEPLYTGINA V, TAX, D M, LOOG M. Multiple instance learning with bag dissimilarities[J], Pattern recognition, 2015, 48 (1): 264-275.
[22] CARBONNEAU M A, GRANGER E, RAYMOND A J, et al. Robust multiple-instance learning ensembles using random subspace instance selection[J]. Pattern recognition, 2016, 58: 83-99.
Similar References:

Memo

-

Last Update: 2018-02-01

Copyright © CAAI Transactions on Intelligent Systems