Name
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. |
Year | Venue | Field |
|---|---|---|
2016 | ECML/PKDD | Online learning,Computer science,Support vector machine,Artificial intelligence,Model compression,Machine learning,Bounded function |
DocType | ISSN | Citations |
Conference | Machine Learning and Knowledge Discovery in Databases. ECML PKDD
2016 | 1 |
PageRank | References | Authors |
0.36 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Kamp | 1 | 17 | 5.80 |
| Sebastian Bothe | 2 | 39 | 4.55 |
| Mario Boley | 3 | 221 | 17.08 |
| Michael Mock | 4 | 175 | 25.30 |