Domain Adaptive Multiple Kernel Learning for Handwritten Digit Recognition

Handwritten character recognition systems suffers from different training and testing sets distributions. In this paper, we propose a two-step domain adaptive multiple kernel learning algorithm, which learns a kernel function based on several kernels in the first step, and trains a target classifier by applying the learned kernel in the second step. Our method can be employed both in semi-supervised and unsupervised domain adaptation cases, while most of the previous domain adaptation methods work only in semi-supervised case. Experiments on adaptation to different databases in this field reveal the superiority of this algorithm in comparison with other adaptation algorithms.