Title :
Kernel machines and additive fuzzy systems: classification and function approximation
Author :
Chen, Yixin ; Wang, James Z.
Author_Institution :
Dept. of Comput. Sci. & Eng., Pennsylvania State Univ., University Park, PA, USA
Abstract :
This paper investigates the connection between additive fuzzy systems and kernel machines. We prove that, under quite general conditions, these two seemingly quite distinct models are essentially equivalent. As a result, algorithms based upon support vector (SV) learning are proposed to build fuzzy systems for classification and function approximation. The performance of the proposed algorithm is illustrated using extensive experimental results.
Keywords :
function approximation; fuzzy systems; operating system kernels; pattern classification; regression analysis; support vector machines; additive fuzzy systems; classification; function approximation; kernel machines; support vector learning algorithm; Computer science; Function approximation; Fuzzy sets; Fuzzy systems; Input variables; Kernel; Paper technology; Support vector machine classification; Support vector machines; Training data;
Conference_Titel :
Fuzzy Systems, 2003. FUZZ '03. The 12th IEEE International Conference on
Print_ISBN :
0-7803-7810-5
DOI :
10.1109/FUZZ.2003.1206530
