MACHINE PERCEPTION AND ROBOTICS GROUP

機械知覚&ロボティクスグループ
中部大学

Random Forest, a multi-class classifier based on statistical learning, is widely used in applications because of its high generalization performance due to randomness. However, in applications such as object detection, disparities in the distributions of the training and test samples from the target scene are often inevitable, resulting in degraded performance. In this case, the training samples need to be reacquired for the target scene, typically at a very high human acquisition cost. To solve this problem, transfer learning has been proposed. In this paper, we present data-level transfer learning for a Random Forest using covariate shift. Experimental results show that the proposed method, called Transfer Forest, can adapt to the target domain by transferring training samples from an auxiliary domain.