Sep
26
“Heterogeneous transfer learning for structured data is a new area of research, which concerns transferring knowledge between different tasks where the data are non-i.i.d. and may be even heterogeneous. This area has emerged as one of the most promising areas in machine learning. In this workshop, we wish to boost the research activities of knowledge transfer across structured data in the machine learning community. We welcome theoretical and applied disseminations that make efforts (1) to expose novel knowledge transfer methodology and frameworks for transfer mining across structured data. (2) to investigate effective (automated, human-machined-cooperated) principles and techniques for acquiring, representing, modeling and engaging transfer learning on structured data in real-world applications.”
Transfer Learning for Structured Data (TLSD-09)
Workshop, in conjunction with NIPS 2009
