Modeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet Process

Transfer learning can be described as the tion of abstract knowledge from one learning domain or task and the reuse of that knowledge in a related domain or task. In categorization settings, transfer learning is the modification by past experience of prior expectations about what types of categories are likely to exist in the world. While transfer learning is an important and active research topic in machine learning, there have been few studies of transfer learning in human categorization. We propose an explanation for transfer learning effects in human categorization, implementing a model from the statistical machine learning literature

• Type: Conference
• Year: 2010
• Where: ICML
• Authors: Kevin R. Canini, Mikhail M. Shashkov, Thomas L. Griffiths
• Keywords: Human Categorization, ICML 2010, Machine Learning, Transfer Learning