Abstract
We propose an efficient distributed online learning protocol for low-latency real-time services. It extends a previously presented protocol to kernelized online learners that represent their models by a support vector expansion. While such learners often achieve higher predictive performance than their linear counterparts, communicating the support vector expansions becomes inefficient for large numbers of support vectors. The proposed extension allows for a larger class of online learning algorithms—including those alleviating the problem above through model compression. In addition, we characterize the quality of the proposed protocol by introducing a novel criterion that requires the communication to be bounded by the loss suffered.
Original language | English |
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Title of host publication | Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings |
Editors | Niels Landwehr, Jilles Giuseppe, Giuseppe Manco, Paolo Frasconi |
Publisher | Springer Verlag |
Pages | 805-819 |
Number of pages | 15 |
ISBN (Print) | 9783319462264 |
DOIs | |
State | Published - 2016 |
Externally published | Yes |
Event | 15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016 - Riva del Garda, Italy Duration: 19 Sep 2016 → 23 Sep 2016 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 9852 LNAI |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016 |
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Country/Territory | Italy |
City | Riva del Garda |
Period | 19/09/16 → 23/09/16 |
Bibliographical note
Publisher Copyright:© Springer International Publishing AG 2016.
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science