id: 06017753
dt: j
an: 06017753
au: Lin, Yih-Lon; Hsieh, Jer-Guang; Wu, Hsu-Kun; Jeng, Jyh-Horng
ti: Three-parameter sequential minimal optimization for support vector
machines.
so: Neurocomputing 74, No. 17, 3467-3475 (2011).
py: 2011
pu: Elsevier Science Publishers B.V., Amsterdam
la: EN
cc:
ut: support vector machine; sequential minimal optimization; classification;
regression
ci:
li: doi:10.1016/j.neucom.2011.06.011
ab: Summary: The well-known sequential minimal optimization (SMO) algorithm is
the most commonly used algorithm for numerical solutions of the support
vector learning problems. At each iteration in the traditional SMO
algorithm, also called 2PSMO algorithm in this paper, it jointly
optimizes only two chosen parameters. The two parameters are selected
either heuristically or randomly, whilst the optimization with respect
to the two chosen parameters is performed analytically. The 2PSMO
algorithm is naturally generalized to the three-parameter sequential
minimal optimization (3PSMO) algorithm in this paper. At each iteration
of this new algorithm, it jointly optimizes three chosen parameters. As
in 2PSMO algorithm, the three parameters are selected either
heuristically or randomly, whilst the optimization with respect to the
three chosen parameters is performed analytically. Consequently, the
main difference between these two algorithms is that the optimization
is performed at each iteration of the 2PSMO algorithm on a line
segment, whilst that of the 3PSMO algorithm on a two-dimensional region
consisting of infinitely many line segments. This implies that the
maximum can be attained more efficiently by 3PSMO algorithm. Main
updating formulae of both algorithms for each support vector learning
problem are presented. To assess the efficiency of the 3PSMO algorithm
compared with the 2PSMO algorithm, 14 benchmark datasets, 7 for
classification and 7 for regression, will be tested and numerical
performances are compared. Simulation results demonstrate that the
3PSMO outperforms the 2PSMO algorithm significantly in both executing
time and computation complexity.
rv: