Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Exploiting Hierarchy in Text Categorization
Information Retrieval
Hierarchical Text Categorization Using Neural Networks
Information Retrieval
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
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
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In this paper we propose a new method for training classifiers for multi-class problems when classes are not (necessarily) mutually exclusive and may be related by means of a probabilistic tree structure. It is based on the definition of a Bayesian model relating network parameters, feature vectors and categories. Learning is stated as a maximum likelihood estimation problem of the classifier parameters. The proposed algorithm is specially suited to situations where each training sample is labeled with respect to only one or part of the categories in the tree. Our experiments on information retrieval scenarios show the advantages of the proposed method.