A new family of online algorithms for category ranking

We describe a new family of topic-ranking algorithms for multi-labeled documents. The motivation for the algorithms stems from recent advances in online learning algorithms. The algorithms we present are simple to implement and are time and memory efficient. We evaluate the algorithms on the Reuters-21578 corpus and the new corpus released by Reuters in 2000. On both corpora the algorithms we present outperform adaptations to topic-ranking of Rocchio's algorithm and the Perceptron algorithm. We also outline the formal analysis of the algorithm in the mistake bound model. To our knowledge, this work is the first to report performance results with the entire new Reuters corpus. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval; I.2.6 [Artificial Intelegence]: Learning--Concept Learning, Connectionism and neural nets General Terms Algorithms, Experimentation,Theory. Keywords Category ranking, Online learning, Perceptrons.

• Type: Journal
• Year: 2002
• Where: SIGIR
• Authors: Koby Crammer, Yoram Singer
• Keywords: Algorithms, Information Technology, Mistake Bound Model, SIGIR 2002, Topic-ranking Algorithms