Exploring Massive Learning via a Prediction System
Source:
AAAI Fall Symposium Series (2007)
Abstract:
We describe the functionality of a large scale system
that, given a stream of characters from a rich source, such as the
pages on the web, engages in repeated prediction and learning. Its
activity includes adding, removing, and updating connection weights
and category nodes. Over time, the system learns to predict better
and acquires new useful categories. In this work, categories are
strings of characters.
The system scales well and the learning is {\em massive}: in the
course of 100s of millions of learning episodes, a few hours on a
single machine, hundreds of thousands of categories and millions of
prediction connections among them are learned.
