New preprint: multi-task training

[Nathan Intrator]

    Making a low-dimensional representation suitable for diverse tasks

    		    Nathan Intrator    Shimon Edelman 
    	              Tel-Aviv U.       Weizmann Ins.

  We introduce a new approach to the training of classifiers for
  performance on multiple tasks. The proposed hybrid training method
  leads to improved generalization via a better low-dimensional
  representation of the problem space. The quality of the
  representation is assessed by embedding it in a 2D space using
  multidimensional scaling, allowing a direct visualization of the
  results. The performance of the approach is demonstrated on a highly
  nonlinear image classification task.

The paper will be described at the comming Transfer workshop at NIPS

url=ftp://cns.brown.edu/nin/papers/mds.ps.Z
or it can be accessed through our hope pages:
   http://www.wisdom.weizmann.ac.il/~edelman/shimon.html
   http://www.physics.brown.edu/~nin

Comments are most welcome.

- Nathan Intrator