{"id":727,"date":"2016-09-16T12:00:00","date_gmt":"2016-09-16T12:00:00","guid":{"rendered":"https:\/\/michaelkamp.org\/?p=727"},"modified":"2016-09-16T12:00:00","modified_gmt":"2016-09-16T12:00:00","slug":"communication-efficient-distributed-online-learning-with-kernels-3","status":"publish","type":"post","link":"https:\/\/michaelkamp.org\/?p=727","title":{"rendered":"Communication-Efficient Distributed Online Learning with Kernels"},"content":{"rendered":"

Michael Kamp, Sebastian Bothe, Mario Boley, Michael Mock: <\/span> Communication-Efficient Distributed Online Learning with Kernels<\/span>. In: <\/span> Frasconi, Paolo; Landwehr, Niels; Manco, Giuseppe; Vreeken, Jilles (Ed.): Machine Learning and Knowledge Discovery in Databases, <\/span>pp. 805\u2013819, <\/span>Springer International Publishing, <\/span>2016<\/span>.<\/span><\/div><\/p>\n

Abstract<\/h2>

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\u2014including 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.<\/p>\n

Links<\/h2>