  The Support Vector Machine using Privileged Information (SVM+) has been
proposed to train a classifier to utilize the additional privileged information
that is only available in the training phase but not available in the test
phase. In this work, we propose an efficient solution for SVM+ by simply
utilizing the squared hinge loss instead of the hinge loss as in the existing
SVM+ formulation, which interestingly leads to a dual form with less variables
and in the same form with the dual of the standard SVM. The proposed algorithm
is utilized to leverage the additional web knowledge that is only available
during training for the image categorization tasks. The extensive experimental
results on both Caltech101 andWebQueries datasets show that our proposed method
can achieve a factor of up to hundred times speedup with the comparable
accuracy when compared with the existing SVM+ method.
