Research on domain-adaptive transfer learning method and its applications

Traditional machine learning methods rely on strong assumptions, especially assuming that training data and testing data in homogeneous feature spaces. However, this is not always true in reality. To break such assumptions, this paper proposes a domain-adaptive transfer learning method, which automatically learns knowledge from existing knowledge bank by extracting linguistic information such as part-of-speech and co-occurrence of keywords and constructing a new domain-adaptive transfer knowledge bank. Through experiments on homogeneous and heterogeneous feature spaces, we testify the efficacy of our methods.