31 August - 2 September 2010, Cumberland Lodge
This workshop has now taken place. The online proceedings are available at http://jmlr.csail.mit.edu/proceedings/papers/v11/, and you can view the talks on Videolectures at http://videolectures.net/wapa2010_cumberland_lodge/.
Pattern Analysis and Statistical Learning cover a wide range of technologies and theoretical frameworks, and significant activity in the past years has resulted in a remarkable convergence and many advances in the theory and principles underlying the field.
Bringing these technologies to real world demanding applications is however often treated as a separate problem, one that does not directly affect the field as a whole. It is instead important to consider the field of Pattern Analysis as fully including all issues involved with the applications of this technology, and hence all issues that arise when deploying, scaling, implementing and using the technology.
We call for constributions in the form of Demos, Case Studies, Working Systems, Real World Applications and Usage Scenarios. Challenges may stem from the violation of common theoretical assumptions, from the specific types of patterns and noise arising in certain scenarios, or from the problem of scaling up the implementation of state of the art algorithms to real world sizes, or from the creation of integrated software systems that contain multiple pattern-analysis components.
We are also interested in new application areas, where Pattern Analysis has been deployed with success, and in issues involving the visualisation and delivery and exploitation of the patterns discovered by PA technologies. Systems working in noisy and unstructured environments and situations are particularly interesting.
The goal is to discuss and reward work aimed at making theory useful and relevant, without requesting the researchers to propose new theoretical methods, but rather requesting to show how they solved the many challenges related to applying these methods to real world scenarios, or how they benefited other fields of research. Getting ideas to work in real scenarios is what this is about.