2003
JMLR
JMLR 2003
A Family of Additive Online Algorithms for Category Ranking
Abstract
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. [abs] [pdf] [ps.gz] [ps]
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Topic Pioneer
— Online Algorithms
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Trend Setter
— Online Algorithms
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Keyword Pioneer
— multi-label classification
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Hot Topic Early Bird
— online learning
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio