PSL 2013 - 2nd International Workshop on Partially Supervised Learning (PSL 2013)

In many applications there are only partial supervision information. For example, in semi-supervised learning there is only supervision information for the labeled examples whereas there is no supervision information for unlabeled data; in multi-instance learning there is only label information for the training bags whereas there is no label information for positive training instances; in weak label setting of multi-label learning, not all labels are known for training examples.
Owing to the fact that most real-world tasks are with partial rather complete supervision information, Partially Supervised Learning (PSL) is of great interest in machine learning, pattern recognition, data mining and related communities. Research in the field of PSL is still in its early stages and has great potential for further growth, thus, leaving plenty of room for further development.
We are pleased to announce that the second Workshop on Partially Supervised Learning will take place at Nanjing University, Nanjing, China. The scope of this workshop includes all kinds of learning paradigms combining unsupervised and supervised learning methods.
This workshop will act as a major forum for international researchers and practitioners working in all areas of partially supervised learning to present and discuss the latest research, results, and ideas in these areas.
Topics
Topics of interest include, but are not limited to:
? Foundations of partially supervised learning
? Learning with unlabeled data
? Learning with incomplete label information
? Learning with latent supervision
? Learning with delayed supervision
? Applications of partially supervised learning
Submission
Original and unpublished contributions are solicited which include regular papers and extended abstracts. Maximum paper length for regular papers is 10 pages (4 pages for extended abstracts) in LNCS/LNAI format. Proceedings will be published by Springer-Verlag as a volume of LNCS/LNAI series.