Exploring Massive Learning via a Prediction System.
Omid Madani · 2007
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 pre-diction and learning. Its activity includes adding, re-moving, and updating connection weights and category nodes. Over time, the system learns to predict better and acquires new useful categories. In this work, cate-gories are strings of characters. The system scales well and the learning is massive: in the course of 100s of millions of learning episodes, a few hours on a single machine, hundreds of thousands of categories and mil-lions of prediction connections among them are learned.