Our distance measures and learning algorithms are based on powerful, elegant and beautiful ideas from the field of Algorithmic Information Theory. While developing our transfer learning mechanisms we also derive results that are interesting in and of themselves. We also developed practical approximations to our formally optimal method for Bayesian decision trees, and applied it to transfer information between 7 arbitrarily chosen data-sets in the UCI machine learning repository through a battery of 144 experiments. The arbitrary choice of databases makes our experiments the most general transfer experiments to date. The experiments also bear out our result that transfer should never hurt too much.The bulk of transfer methods developed to date can be divided into one of two distinct categories. The first is intra-domain transfer, where the primary focus is on transferring information between tasks defined on the same input-output space, anbsp;...
|Title||:||Universal Transfer Learning|
|Author||:||M. M. Hassan Mahmud|
|Publisher||:||ProQuest - 2008|