RBF kernel based support vector machine with universal approximation and its application

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3173, Issue , 2004, Pages 512-517

  Wang, Junping ^a   Chen, Quanshi ^a   Chen, Yong ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

FUNCTIONS; NOISE POLLUTION; RADIAL BASIS FUNCTION NETWORKS;

ARBITRARY FUNCTIONS; COMPACT SETS; DISCRETE FUNCTIONS; GENERALIZATION ABILITY; ITS APPLICATIONS; NONLINEAR FUNCTIONS; UNIVERSAL APPROXIMATION; UNIVERSAL APPROXIMATION PROPERTIES;

SUPPORT VECTOR MACHINES;

EID: 35048829777     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-28647-9_85     Document Type: Article

References (4)

1. Johan A.K. Suykens: Nonlinear Modeling and Support Vector Machines. IEEE Conference on Instrumentation and Measurement Technology. Budapest, Hungary, (2001) pp.287-294, May.
2. Johan A.K. Suykens: Sparse Approximation Using Least Squares Support Vector Machines. IEEE Int. Symposium on Circuit and Systems. Geneva, Switzerland (2000) pp.757-760, May.
3. Feng Rui: Soft Sensor Modeling Based on Support Vector Machine. Information and Control. (2002) vol.31, no.6, pp.567-571.
4. Wang J P, Jing Z L: A Stochastic Fuzzy Neural Network with Universal Approximation and Its Application. Proc. of Int. Conf. On Fuzzy Information Processing Theories and Applications. Beijing: Tsinghua University Press & Springer, (2003) pp. 497-502.