Machine Learning - Special issue on inductive transfer
Exploiting Hierarchy in Text Categorization
Information Retrieval
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Hierarchical Text Classification and Evaluation
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Building Hierarchical Classifiers Using Class Proximity
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Large margin hierarchical classification
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Hierarchical document categorization with support vector machines
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Kernel-Based Learning of Hierarchical Multilabel Classification Models
The Journal of Machine Learning Research
Exploiting known taxonomies in learning overlapping concepts
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A survey of hierarchical classification across different application domains
Data Mining and Knowledge Discovery
Hierarchical text classification with latent concepts
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
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Text categorization is a crucial and well-proven method for organizing the collection of large scale documents. In this paper, we propose a hierarchical multi-class text categorization method with global margin maximization. We not only maximize the margins among leaf categories, but also maximize the margins among their ancestors. Experiments show that the performance of our algorithm is competitive with the recently proposed hierarchical multi-class classification algorithms.