Hierarchical SVM Algorithm

A Hierarchical SVM Algorithm is an SVM algorithm that can solve a hierarchical structure prediction task.

• Context:
  • It can be implemented int an Hierarchical SVM System[1].
• See: Hierarchical SVM Model.

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References

2011

• (Xiao et al., 2011) ⇒ Lin Xiao, Dengyong Zhou, and Mingrui Wu. “Hierarchical classification via orthogonal transfer.” In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 801-808. 2011.
  • ABSTRACT: We consider multiclass classification problems where the set of labels are organized hierarchically as a category tree. We associate each node in the tree with a classifier and classify the examples recursively from the root to the leaves. We propose a hierarchical Support Vector Machine (SVM) that encourages the classifier at each node to be different from the classifiers at its ancestors. More specifically, we introduce regularizations that force the normal vector of the classifying hyperplane at each node to be orthogonal to those at its ancestors as much as possible. We establish conditions under which training such a hierarchical SVM is a convex optimization problem, and develop an efficient dual-averaging method for solving it.

2004

• (Cai & Hofmann, 2004) ⇒ Lijuan Cai, and Thomas Hofmann. (2004). “Hierarchical Document Categorization with Support Vector Machines.” In: Proceedings of the thirteenth ACM International Conference on Information and knowledge management. ISBN:1-58113-874-1 doi:10.1145/1031171.1031186
• (Tsochantaridis et al., 2004) ⇒ Ioannis Tsochantaridis, Thomas Hofmann, Thorsten Joachims, and Yasemin Altun. (2004). “Support Vector Machine Learning for Interdependent and Structured Output Spaces.” In: Proceedings of the twenty-first International Conference on Machine learning. ISBN:1-58113-838-5 doi:10.1145/1015330.1015341

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