  We investigate two training-set methods: support vector machines (SVMs) and
Kernel Regression (KR) for photometric redshift estimation with the data from
the Sloan Digital Sky Survey Data Release 5 and Two Micron All Sky Survey
databases. We probe the performances of SVMs and KR for different input
patterns. Our experiments show that the more parameters considered, the
accuracy doesn't always increase, and only when appropriate parameters chosen,
the accuracy can improve. Moreover for different approaches, the best input
pattern is different. With different parameters as input, the optimal bandwidth
is dissimilar for KR. The rms errors of photometric redshifts based on SVM and
KR methods are less than 0.03 and 0.02, respectively. Finally the strengths and
weaknesses of the two approaches are summarized. Compared to other methods of
estimating photometric redshifts, they show their superiorities, especially KR,
in terms of accuracy.
