4 citations · 13 across the 8 of their papers we have counts for
5 papers · 1 filter
Distributed Online Learning with Multiple Kernels
Jeongmin Chae, Songnam Hong
We consider the problem of learning a nonlinear function over a network of learners in a fully decentralized fashion. Online learning is additionally assumed, where every learner r…
Multiple Kernel-Based Online Federated Learning
Jeongmin Chae, Songnam Hong
Online federated learning (OFL) becomes an emerging learning framework, in which edge nodes perform online learning with continuous streaming local data and a server constructs a g…
Distributed Online Learning with Multiple Kernels
Jeongmin Chae, Songnam Hong
In the Internet-of-Things (IoT) systems, there are plenty of informative data provided by a massive number of IoT devices (e.g., sensors). Learning a function from such data is of…
Pool-based sequential active learning with multi kernels
Jeongmin Chae, Songnam Hong
We study a pool-based sequential active learning (AL), in which one sample is queried at each time from a large pool of unlabeled data according to a selection criterion. For this…
Active Learning with Multiple Kernels
Songnam Hong, Jeongmin Chae
Online multiple kernel learning (OMKL) has provided an attractive performance in nonlinear function learning tasks. Leveraging a random feature approximation, the major drawback of…