Exponentiated gradient versus gradient descent for linear predictors
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Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Ultraconservative online algorithms for multiclass problems
The Journal of Machine Learning Research
Online multiclass learning by interclass hypothesis sharing
ICML '06 Proceedings of the 23rd international conference on Machine learning
Google's PageRank and Beyond: The Science of Search Engine Rankings
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The Computer Journal
Online Passive-Aggressive Algorithms
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Ontologies are us: a unified model of social networks and semantics
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
JeromeDL – adding semantic web technologies to digital libraries
DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications
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The emergence of Web 2.0 has created a lot of annotation and personalization information about web resources. Extracting and utilizing these information to enhance the quality of services is a key target of modern digital libraries. In this paper, we present a novel Automatic Document Tagging (ADT) approach for digital libraries. In our approach, the ADT problem is formulated as a variant of multi-class classification problem. But differently, the training data for ADT is collected from the user's historic tags and only partially labeled. The incompleteness of the training data makes the training a more challenging problem. To overcome this problem, an efficient randomized online training algorithm (RPL) is proposed. RPL algorithm has two phases: (i) random exploitation and (ii) classifier update. The experimental results from both synthetic and real-word data demonstrate the effectiveness.