Transfer learning based on the observation probability of each attribute
Masahiro Suzuki, Haruhiko Sato, Satoshi Oyama, Masahito Kurihara · 2014
Machine learning is the basis of important advances in artificial intelligence. Unlike the general methods of machine learning, which use the same tasks for training and testing, the method of transfer learning uses different tasks to learn a new task. Among the various transfer learning algorithms in the literature, we focus on the attribute-based transfer learning. This algorithm realizes transfer learning by introducing attributes and transferring the results of training to another task with the common attributes. However, the existing method does not consider the frequency in which each attribute appears in feature vectors (called the observation probability). In this paper, we present a generative model with the observation probability. By the experiments, we show that the proposed method has achieved a higher accuracy rate than the existing method. Moreover, we see that it makes possible the incremental learning that was impossible in the existing method.