BoosTexter is a general purpose machine-learning program based on boosting for building a classifier from text and/or attribute-value data. BoosTexter can handle:
Note, however, that BoosTexter uses boosting on top of very simple decision rules (sometimes called "decision stumps"). Although this allows BoosTexter to run very fast while often giving highly accurate results, this approach may not be appropriate for all learning tasks. For instance, boosting on top of decision trees (such as C4.5 or CART) may be more effective for some applications.
More detailed information about BoosTexter is available here:
The object code for BoosTexter is available free from AT&T for non-commercial research or educational purposes by clicking here and then following the "boostexter" link at the bottom of that page.
BoosTexter was written by Erin Allwein, Robert Schapire, and Yoram Singer.