TY - JOUR
AU - Shilton, A
AU - Lai, DTH
AU - Palaniswami, M
Y2 - 2014/05/21
Y1 - 2010/04/01
SN - 1083-4419
UR - http://hdl.handle.net/11343/27666
AB - In this paper, division algebras are proposed as an elegant basis upon which to extend support vector regression (SVR) to multidimensional targets. Using this framework, a multitarget SVR called epsilon(Z)-SVR is proposed based on an epsilon-insensitive loss function that is independent of the coordinate system or basis used. This is developed to dual form in a manner that is analogous to the standard epsilon-SVR. The epsilon(H)-SVR is compared and contrasted with the least-square SVR (LS-SVR), the Clifford SVR (C-SVR), and the multidimensional SVR (M-SVR). Three practical applications are considered: namely, 1) approximation of a complex-valued function; 2) chaotic time-series prediction in 3-D; and 3) communication channel equalization. Results show that the epsilon(H)-SVR performs significantly better than the C-SVR, the LS-SVR, and the M-SVR in terms of mean-squared error, outlier sensitivity, and support vector sparsity.
LA - English
PB - IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
KW - Electrical and Electronic Engineering not elsewhere classified; Expanding Knowledge in Engineering
T1 - A Division Algebraic Framework for Multidimensional Support Vector Regression
DO - 10.1109/TSMCB.2009.2028314
IS - IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
VL - 40
IS - 2
SP - 517-528
L1 - /bitstream/handle/11343/27666/253830_7082.pdf?sequence=1&isAllowed=n
ER -