Journal of Computer and System Sciences
Pivoted document length normalization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Feature selection, perceptron learning, and a usability case study for text categorization
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Large margin classification using the perceptron algorithm
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Information Retrieval
Indexing for fast categorisation
ACSC '03 Proceedings of the 26th Australasian computer science conference - Volume 16
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
An analysis of the relative hardness of Reuters-21578 subsets: Research Articles
Journal of the American Society for Information Science and Technology
Multi-labelled classification using maximum entropy method
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Collective multi-label classification
Proceedings of the 14th ACM international conference on Information and knowledge management
NEWPAR: an automatic feature selection and weighting schema for category ranking
Proceedings of the 2006 ACM symposium on Document engineering
Flexible text segmentation with structured multilabel classification
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Efficient Learning of Label Ranking by Soft Projections onto Polyhedra
The Journal of Machine Learning Research
Learning to rank at query-time using association rules
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Ml-rbf: RBF Neural Networks for Multi-Label Learning
Neural Processing Letters
NEWPAR: An Optimized Feature Selection and Weighting Schema for Category Ranking
Proceedings of the 2006 conference on STAIRS 2006: Proceedings of the Third Starting AI Researchers' Symposium
Feature selection for multi-label naive Bayes classification
Information Sciences: an International Journal
Semi-supervised multi-label learning by constrained non-negative matrix factorization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Preferential text classification: learning algorithms and evaluation measures
Information Retrieval
An Evidence-Theoretic k-Nearest Neighbor Rule for Multi-label Classification
SUM '09 Proceedings of the 3rd International Conference on Scalable Uncertainty Management
Multi-label learning by instance differentiation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Database-text alignment via structured multilabel classification
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
On the importance of parameter tuning in text categorization
PSI'06 Proceedings of the 6th international Andrei Ershov memorial conference on Perspectives of systems informatics
Medical coding classification by leveraging inter-code relationships
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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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.