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Author Woolam, C. ♦ Khan, L.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2008
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Ontology ♦ Buildings ♦ Ontologies ♦ Data mining ♦ Intelligent agent ♦ Automatic speech recognition ♦ Semantic Web ♦ Perceptron ♦ Databases ♦ Aggregates ♦ Classifcation ♦ Prediction algorithms ♦ Aerospace safety
Abstract Previous work in hierarchical categorization focuses on the hierarchical perceptron (Hieron) algorithm. Hierarchical perceptron works on the principles of the perceptron,that is each class label in the hierarchy has an associated weight vector. To account for the hierarchy, we begin at the root of the tree and sum all weights to the target label.We make a prediction by considering the label that yields the maximum inner product of its feature set with its path-summed weights. Learning is done by adjusting the weights along the path from the predicted node to the correct node by a specific loss function that adheres to the large margin principal. There are several problems with applying this approach to a multiple class problem. In many cases we could end up punishing weights that gave a correct prediction, because the algorithm can only take a single case at a time. In this paper we present an extended hierarchical perceptron algorithm capable of solving the multiple categorization problem (MultiHieron). We introduce new aggregate loss function for multiple label learning. We make weight updates simultaneously instead of serially. Then, significant improvement over the basic Hieron algorithm is demonstrated on the aviation safety reporting system (ASRS) flight anomaly database and OntoNews corpus using both flat and hierarchical categorization metrics.
Description Author affiliation: Univ. of Texas at Dallas, Dallas, TX (Woolam, C.; Khan, L.)
ISBN 9780769534961
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2008-12-09
Publisher Place Australia
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 341.91 kB
Page Count 5
Starting Page 570
Ending Page 574


Source: IEEE Xplore Digital Library