A Family of Additive Online Algorithms for Category Ranking

Koby Crammer, Yoram Singer

Research output: Contribution to journalArticlepeer-review

139 Scopus citations


We describe a new family of topic-ranking algorithms for multi-labeled documents. The motivation for the algorithms stem from recent advances in online learning algorithms. The algorithms are simple to implement and are also time and memory efficient. We provide a unified analysis of the family of algorithms in the mistake bound model. We then discuss experiments with the proposed family of topic-ranking algorithms on the Reuters-21578 corpus and the new corpus released by Reuters in 2000. On both corpora, the algorithms we present achieve state-of-the-art results and outperforms topic-ranking adaptations of Rocchio's algorithm and of the Perceptron algorithm.

Original languageEnglish (US)
Pages (from-to)1025-1058
Number of pages34
JournalJournal of Machine Learning Research
Issue number6
StatePublished - Aug 15 2003

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence


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