Compact Coding for Hyperplane Classifiers in Heterogeneous Environment
published: Oct. 3, 2011, recorded: September 2011, views: 3068
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Transfer learning techniques have witnessed a significant development in real applications where the knowledge from previous tasks are required to reduce the high cost of inquiring the labeled information for the target task. However, how to avoid negative transfer which happens due to different distributions of tasks in heterogeneous environment is still a open problem. In order to handle this kind of issue, we propose a Compact Coding method for Hyperplane Classifiers (CCHC) under a two-level framework in inductive transfer learning setting. Unlike traditional methods, we measure the similarities among tasks from the macro level perspective through minimum encoding. Particularly speaking, the degree of the similarity is represented by the relevant code length of the class boundary of each source task with respect to the target task. In addition, informative parts of the source tasks are adaptively selected in the micro level viewpoint to make the choice of the specific source task more accurate. Extensive experiments show the effectiveness of our algorithm in terms of the classification accuracy in both UCI and text data sets.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !