Rough support vector machine and its application to wastewater treatment processes

Kongzhi yu Juece/Control and Decision

Volumn 19, Issue 5, 2004, Pages 573-576

  Fan, Xin Wei ^a   Du, Shu Xin ^a   Wu, Tie Jun ^a  

^a STATE KEY LABORATORY OF INDUSTRIAL CONTROL TECHNOLOGY   (China)

Author keywords

Classification accuracy; Rough set; Support vector machine; Wastewater treatment process

Indexed keywords

CLASSIFICATION (OF INFORMATION); CONDITION MONITORING; NUMERICAL ANALYSIS; ROUGH SET THEORY; WASTEWATER TREATMENT;

CLASSIFICATION ACCURACY; SUPPORT VECTOR MACHINE; WASTEWATER TREATMENT PROCESS;

LEARNING SYSTEMS;

EID: 3543138313     PISSN: 10010920     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (9)

1. V Vapnik. The Nature of Statistical Learning Theory [M]. New York: Springer-Verlag, 1995.
2. Kreßel U. Pairwise classification and support vector machines [A]. Advances in Kernel Methods Support Vector Learning [C], Cambridge: MIT Press, 1999. 255-268.
3. Joachims T. Text categorization with support vector machines [R]. Dortmund: University of Dortmund, 1997.
4. Cai Y D, Liu X J, Xu X B, et al. Prediction of protein structural classes by support vector machines [J]. Computers and Chemistry, 2002, 26(3): 293-296.
5. Pawlak Z. Rough Sets-theoretical Aspects of Reasoning about Data [M]. Boston, London: Kluwer Academic Publishers, 1992. 1-53.
6. Aleksander Ohrn. Discernibility and rough sets in medicine: Tools and applications [D]. Trondheim: Norwegian University of Science and Technology, 1999. 53,63-65.
7. Scholkopf B, Smola A, Williamson R C, et al. New support vector algorithms [J]. Neural Computation, 2000, 12(5): 1207-1245.
8. Hsn C W, Lin C J. A comparison of methods for multiclass support vector machines [J]. IEEE Trans on Neural Networks, 2002, 13(2): 415-425.
9. Chang C C, Lin C J. Training v-support vector classifiers: Theory and algorithms [J]. Neural Computation, 2001, 13(9): 2119-2147.