A lossy counting based approach for learning on streams of graphs on a budget

Giovanni Da San Martino, Nicolò Navarin, Alessandro Sperduti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

In many problem settings, for example on graph domains, online learning algorithms on streams of data need to respect strict time constraints dictated by the throughput on which the data arrive. When only a limited amount of memory (budget) is available, a learning algorithm will eventually need to discard some of the information used to represent the current solution, thus negatively affecting its classification performance. More importantly, the overhead due to budget management may significantly increase the computational burden of the learning algorithm. In this paper we present a novel approach inspired by the Passive Aggressive and the Lossy Counting algorithms. Our algorithm uses a fast procedure for deleting the less influential features. Moreover, it is able to estimate the weighted frequency of each feature and use it for prediction.

Original languageEnglish
Title of host publicationIJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence
Pages1294-1301
Number of pages8
Publication statusPublished - 1 Dec 2013
Event23rd International Joint Conference on Artificial Intelligence, IJCAI 2013 - Beijing, China
Duration: 3 Aug 20139 Aug 2013

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Other

Other23rd International Joint Conference on Artificial Intelligence, IJCAI 2013
CountryChina
CityBeijing
Period3/8/139/8/13

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ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Da San Martino, G., Navarin, N., & Sperduti, A. (2013). A lossy counting based approach for learning on streams of graphs on a budget. In IJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence (pp. 1294-1301). (IJCAI International Joint Conference on Artificial Intelligence).