Hierarchical supervised classifiers are highly demanding in terms of labelled examples, because the number of categories are proportional to the size of a given taxonomy. In this case the bootstrapping process may represent a critical bottleneck for the deployment of learning techniques and the training of supervised classifiers. This work proposes a method to make a first hypothesis of categorization for a set of unlabelled documents with respect to a given empty hierarchy of concepts. The goal is to support a semi-automated management of the bootstrapping. The proposed solution is based on a revised model of self-organizing maps, namely TaxSOM, in such a way that the unsupervised learning is biased by a taxonomy given as input to the model

Bootstrapping of Supervised Hierarchical Classifiers

Adami, Giordano;Avesani, Paolo;Sona, Diego
2003-01-01

Abstract

Hierarchical supervised classifiers are highly demanding in terms of labelled examples, because the number of categories are proportional to the size of a given taxonomy. In this case the bootstrapping process may represent a critical bottleneck for the deployment of learning techniques and the training of supervised classifiers. This work proposes a method to make a first hypothesis of categorization for a set of unlabelled documents with respect to a given empty hierarchy of concepts. The goal is to support a semi-automated management of the bootstrapping. The proposed solution is based on a revised model of self-organizing maps, namely TaxSOM, in such a way that the unsupervised learning is biased by a taxonomy given as input to the model
2003
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/2483
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