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Found 6 papers, 0 papers with code
Matrix Approximation with Side Information: When Column Sampling is Enough
A theoretical spectral error bound is provided, which captures the possible inaccuracies of the side information.
Distributed Online Learning with Multiple Kernels
We consider the problem of learning a nonlinear function over a network of learners in a fully decentralized fashion.
Multiple Kernel-Based Online Federated Learning
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 global model from the aggregated local models.
Distributed Online Learning with Multiple Kernels
Learning a function from such data is of great interest in machine learning tasks for IoT systems.
Pool-based sequential active learning with multi kernels
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.
Active Learning with Multiple Kernels
In this paper, we introduce a new research problem, termed (stream-based) active multiple kernel learning (AMKL), in which a learner is allowed to label selected data from an oracle according to a selection criterion.
