  In this paper, we propose the problem of domain transfer structured output
learn- ing and the first solution to solve it. The problem is defined on two
different data domains sharing the same input and output spaces, named as
source domain and target domain. The outputs are structured, and for the data
samples of the source domain, the corresponding outputs are available, while
for most data samples of the target domain, the corresponding outputs are
missing. The input distributions of the two domains are significantly
different. The problem is to learn a predictor for the target domain to predict
the structured outputs from the input. Due to the limited number of outputs
available for the samples form the target domain, it is difficult to directly
learn the predictor from the target domain, thus it is necessary to use the
output information available in source domain. We propose to learn the target
domain predictor by adapting a auxiliary predictor trained by using source
domain data to the target domain. The adaptation is implemented by adding a
delta function on the basis of the auxiliary predictor. An algorithm is
developed to learn the parameter of the delta function to minimize loss
functions associat- ed with the predicted outputs against the true outputs of
the data samples with available outputs of the target domain.
