** Next:** Introduction

# Active Learning with Statistical Models

**
David A. Cohn**

Zoubin Ghahramani

Michael I. Jordan

**
Center for Biological and Computational Learning **

Dept. of Brain and Cognitive Sciences

Massachusetts Institute of Technology

Cambridge, MA 02139 USA

### Abstract:

*For many types of machine learning algorithms, one can compute the
statistically ``optimal'' way to select training data. In this paper,
we review how optimal data selection techniques have been used with
feedforward neural networks. We then show how the same principles may
be used to select data for two alternative, statistically-based
learning architectures: mixtures of Gaussians and locally weighted
regression. While the techniques for neural networks are
computationally expensive and approximate, the techniques for mixtures
of Gaussians and locally weighted regression are both efficient and
accurate. Empirically, we observe that the optimality criterion
sharply decreases the number of training examples the learner needs in
order to achieve good performance.
*

*David Cohn *

Mon Mar 25 09:20:31 EST 1996