  The massive photometric data collected from multiple large-scale sky surveys
offer significant opportunities for measuring distances of celestial objects by
photometric redshifts. However, catastrophic failure is still an unsolved
problem for a long time and exists in the current photometric redshift
estimation approaches (such as $k$-nearest-neighbor). In this paper, we propose
a novel two-stage approach by integration of $k$-nearest-neighbor (KNN) and
support vector machine (SVM) methods together. In the first stage, we apply KNN
algorithm on photometric data and estimate their corresponding z$_{\rm phot}$.
By analysis, we find two dense regions with catastrophic failure, one in the
range of z$_{\rm phot}\in[0.3,1.2]$, the other in the range of z$_{\rm phot}\in
[1.2,2.1]$. In the second stage, we map the photometric input pattern of points
falling into the two ranges from original attribute space into a high
dimensional feature space by Gaussian kernel function in SVM. In the high
dimensional feature space, many outlier points resulting from catastrophic
failure by simple Euclidean distance computation in KNN can be identified by a
classification hyperplane of SVM and further be corrected. Experimental results
based on the SDSS (the Sloan Digital Sky Survey) quasar data show that the
two-stage fusion approach can significantly mitigate catastrophic failure and
improve the estimation accuracy of photometric redshifts of quasars. The
percents in different |$\Delta$z| ranges and rms (root mean square) error by
the integrated method are $83.47\%$, $89.83\%$, $90.90\%$ and 0.192,
respectively, compared to the results by KNN ($71.96\%$, $83.78\%$, $89.73\%$
and 0.204).
