Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Improving Text Classification by Shrinkage in a Hierarchy of Classes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
Introduction: Special issue on neural networks and kernel methods for structured domains
Neural Networks - Special issue on neural networks and kernel methods for structured domains
Hierarchical classification: combining Bayes with SVM
ICML '06 Proceedings of the 23rd international conference on Machine learning
Acclimatizing Taxonomic Semantics for Hierarchical Content Classification
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Methodological Review: Towards knowledge-based gene expression data mining
Journal of Biomedical Informatics
Topic taxonomy adaptation for group profiling
ACM Transactions on Knowledge Discovery from Data (TKDD)
Text Learning and Hierarchical Feature Selection in Webpage Classification
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Hierarchical Classification of Web Pages Using Support Vector Machine
ICADL 08 Proceedings of the 11th International Conference on Asian Digital Libraries: Universal and Ubiquitous Access to Information
Ml-rbf: RBF Neural Networks for Multi-Label Learning
Neural Processing Letters
Multi-class Boosting with Class Hierarchies
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Feature selection for multi-label naive Bayes classification
Information Sciences: an International Journal
Multi-label learning by instance differentiation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Exploiting known taxonomies in learning overlapping concepts
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The kernelHMM: learning kernel combinations in structured output domains
Proceedings of the 29th DAGM conference on Pattern recognition
A Study of Hierarchical and Flat Classification of Proteins
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A survey of hierarchical classification across different application domains
Data Mining and Knowledge Discovery
Multi-instance multi-label learning
Artificial Intelligence
Decision trees for hierarchical multilabel classification: a case study in functional genomics
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Computers and Industrial Engineering
A correlation approach for automatic image annotation
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Exploiting concept clumping for efficient incremental news article categorization
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-Label Classification Method for Multimedia Tagging
International Journal of Multimedia Data Engineering & Management
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We present a kernel-based algorithm for hierarchical text classification where the documents are allowed to belong to more than one category at a time. The classification model is a variant of the Maximum Margin Markov Network framework, where the classification hierarchy is represented as a Markov tree equipped with an exponential family defined on the edges. We present an efficient optimization algorithm based on incremental conditional gradient ascent in single-example subspaces spanned by the marginal dual variables. Experiments show that the algorithm can feasibly optimize training sets of thousands of examples and classification hierarchies consisting of hundreds of nodes. The algorithm's predictive accuracy is competitive with other recently introduced hierarchical multi-category or multilabel classification learning algorithms.