Transductive Experimental Design (TED)
· Short
Introduction:
TED is a very simple and effective
algorithm to select the most informative experiments x to get measurements
y for learning a regression model y = f(x). We demonstrated in the special case
of square error loss, the active learning is independent to measurements
(labels). This allows us to design a very simple active learning algorithm (see
the Algorithm 1 in our ICML paper) that avoids expensive retraining and can be
implemented by only several lines of matlab codes. Though TED is designed for
least-squares regression, it has shown very good results for also
classification tasks, for example, text categorization.
· Active Learning
via Transductive Experimental Design
Proceedings of the
23rd International Conference on Machine
Learning (ICML 2006), July,
2006
· Matlab
Code and Data: actvted_demo.zip (2.2 MB, including a toy problem
and an experiment on a text corpus benchmark),
readme.txt