Author: Dayne Freitag

We describe a learning apprentice system, called WebWatcher, which both performs the kind of indexing used by Web catalogers like Lycos, and attempts to exploit the two sources of knowledge listed above.

This paper describes the first implementation of WebWatcher, a Learning Apprentice for the World Wide Web.

Personal software assistants that help users with tasks like finding information, scheduling calendars, or managing workflow will require significant customization.

We examine five greedy hillclimbing procedures that search for attribute sets that generalize well with ID3/C4.5. Experiments suggest hillclimbing in attribute space can yield substantial improvements in generalization performance.