Generalizing from several related classification tasks to a new
Gilles Blanchard, Gyemin Lee and Clayton Scott
Neural Information Processing Systems (NIPS)
We consider the problem of assigning class labels to an unlabeled test
data set, given several labeled training data sets drawn from similar
distributions. This problem arises in several applications where data
distributions fluctuate because of biological, technical, or other sources
of variation. We develop a distribution-free, kernel-based approach to the
problem. This approach involves identifying an appropriate reproducing
kernel Hilbert space and optimizing a regularized empirical risk over the
space. We present generalization error analysis, describe universal
kernels, and establish universal consistency of the proposed methodology.
Experimental results on flow cytometry data are presented.