Lin Yao, Tian Jie, Zhang Xiao-peng. Application of a new medical image segmentation method based on fuzzy connectedness and FCM [J]. Chinese Journal of Stereology and Image Analysis, 2001,6(2):103～108.[林瑶,田捷,张晓鹏.基于模糊连接度的FCM分割方法在医学图像分析中的应用[J].中国体视学与图像分析,2001,6(2):103～108.]
[2]
Vapnik V. The Nature of Statistical Learning Theory [M ]. New York: Springer-Verlag, 1995.
[3]
Burges C. A tutorial on support vector machines for pattern recognition [ J ]. Data Mining and Knowledge Discovery, 1998,2 (2):121 ～ 167.
[4]
Zhao Q, Principe J. Support vector machines for SAR automatic target recognition [ J ]. IEEE Transactions on Aerospace and Electronic Systems, 2001, 37 (2): 643 ～ 654.
[5]
Weston J, Watkins C. Multi-class Support Vector Machines [ R ].Technical Report, CSD-TR-98-04, Egham, UK: University of London, 1998:1 ～9.
[6]
Hsu C W, Lin C J. A comparison of methods for multi-class support vector machines[ J]. IEEE Transactions on Neural Networks, 2002,13(2): 415 ～425.
[7]
Haralick R M, Shanmugam K, Dinstein I. Textual features for image classification [ J ]. IEEE Transactions on Systems, Man and Cybernetics, 1973, 3(6): 610 ～621.
[8]
Muller K R, Mika S, Ratsch G, et al. An introduction to kernelbased learning algorithm[ J]. IEEE Transactions on Neural Networks,2001, 12(2): 181 ～201.
[9]
Reddick W E, Glass J O, Cook E N, et al. Automated segmentation and classification of multispectral magnetic resonance images of brain using artificial neural networks [ J ]. IEEE Transactions on Medical Imaging, 1997, 16(6): 911 ～918.
[10]
Zhang Xue-gong. Introduction to statistical learning theory and support vector machines[ J]. Acta Automatica Sinica, 2000,26( 1 ):32～42.[张学工.关于统计学习理论与支持向量机[J].自动化学报,2000,26(1):32～42.]
[11]
Osuna E, Freund R, Girosi F. Training support vector machines: An application to face detection [ A ]. In: Proceedings of Computer Vision and Pattern Recognition [ C ], San Juan, Puerto Rico, 1997:130 ～ 136.
[12]
Lin C F, Wang S D. Fuzzy support vector machines [ J]. IEEE Transactions on Neural Networks, 2002, 13 (2): 464 ～ 471.
[13]
Genton M G. Classes of kernels for machine learning: a statistics perspective [ J]. Journal of Machine Learning Research, 2001,2(2): 299 ～312.
[14]
Petersen M E, Pelikan E. Detection of bone tumouts in radiographic images using neural networks [ J ]. Pattern Analysis & Application,1999, 2(2): 172 ～183.
[15]
Brailovsky V L, Barzilay O, Shahave R. On global, local, mixed and neighborhood kernels for support vector machines [ J ]. Pattern Recognition Letters, 1999, 20 ( 11-13 ): 1183 ～ 1190.
