Emacs Lisp in Edwin SScheme

The MIT-Scheme program development environment includes a general-purpose text editor, Edwin, that has an extension language, Edwin Scheme. Edwin is very similar to another general-purpose text editor, GNU Emacs, which also has an extension language, Emacs Lisp. The popularity of GNU Emacs has lead to a large library of tools written in Emacs Lisp. The goal of this thesis is to implement a useful subset of Emacs Lisp in Edwin Scheme. This subset was chosen to be sufficient for simple operation of the GNUS news reading program.

Active Learning with Statistical Models

For many types of learners one can compute the statistically 'optimal' way to select data. We review how these 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 expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regression are both efficient and accurate.