Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical Text Categorization Using Neural Networks
Information Retrieval
Improving Text Classification by Shrinkage in a Hierarchy of Classes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Hierarchical Text Classification and Evaluation
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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
Support vector machines classification with a very large-scale taxonomy
ACM SIGKDD Explorations Newsletter - Natural language processing and text mining
A Discriminative Learning Framework with Pairwise Constraints for Video Object Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accelerated training of conditional random fields with stochastic gradient methods
ICML '06 Proceedings of the 23rd international conference on Machine learning
Incremental Algorithms for Hierarchical Classification
The Journal of Machine Learning Research
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
On the value of pairwise constraints in classification and consistency
Proceedings of the 24th international conference on Machine learning
Classification with partial labels
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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In this paperwe address the problem of semi-supervised hierarchical learning when some cases are fully labeled while other cases are only partially labeled, named Hierarchical Partial Labels. Given a label hierarchy, a fully labeled example provides a path from the root node to a leaf node while a partially labeled example only provides a path from the root node to an internal node. We introduce a discriminative learning approach, called Partial HSVM, that incorporates partially labeled information into the hierarchical maximum margin-based learning framework. The partially labeled hierarchical learning problem is formulated as a quadratic optimization that minimizes the empirical risk with L2-norm regularization. We also present an efficient algorithm for the hierarchical classification in the presence of partially labeled information. In our experiments with the WIPO-alpha patent collection, we compare our proposed algorithm with two other baseline approaches: Binary HSVM, a standard approach to hierarchical classification, which builds a binary classifier (SVM) at each node in the hierarchy, and PL-SVM, a flat multiclass classifier which can take advantages of the partial label information. Our empirical results show that Partial HSVM outperforms Binary HSVM and PL-SVM across different performance metrics. The experimental results demonstrate that our proposed algorithm, Partial HSVM, combines the strength of both methods, the Binary HSVM and PL-SVM, since it utilizes both the hierarchical information and the partially labeled examples. In addition, we observe the positive correlation between the labeling effort in obtaining partially labeled data and the improvement in performance.