NDLI: Efficient Kernel-based Learning for Trees

Content Provider	IEEE Xplore Digital Library
Author	Aiolli, F. Da San Martino, G. Sperduti, A. Moschitti, A.
Copyright Year	2007
Description	Author affiliation: Dipt. di Matematica Pura ed Applicata, Universita di Padova (Aiolli, F.; Da San Martino, G.; Sperduti, A.)
Abstract	Kernel methods are effective approaches to the modeling of structured objects in learning algorithms. Their major drawback is the typically high computational complexity of kernel functions. This prevents the application of computational demanding algorithms, e.g. support vector machines, on large datasets. Consequently, on-line learning approaches are required. Moreover, to facilitate the application of kernel methods on structured data, additional efficiency optimization should be carried out. In this paper, we propose direct acyclic graphs to reduce the computational burden and storage requirements by representing common structures and feature vectors. We show the benefit of our approach for the perceptron algorithm using tree and polynomial kernels. The experiments on a quite extensive dataset of about one million of instances show that our model makes the use of kernels for trees practical. From the accuracy point of view, the possibility of using large amount of data has allowed us to reach the state-of-the-art on the automatic detection of semantic role labeling as defined in the conference on natural language learning shared task
Starting Page	308
Ending Page	315
File Size	252131
Page Count	8
File Format	PDF
ISBN	1424407052
DOI	10.1109/CIDM.2007.368889
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-03-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Algorithm design and analysis Machine learning algorithms Tree graphs Classification algorithms Data mining Kernel Bioinformatics Computational complexity Computational intelligence
Content Type	Text
Resource Type	Article

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