Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning

NeurIPS 2019 Weishi ShiQi Yu

We propose a novel active learning (AL) model that integrates Bayesian and discriminative kernel machines for fast and accurate multi-class data sampling. By joining a sparse Bayesian model and a maximum margin machine under a unified kernel machine committee (KMC), the proposed model is able to identify a small number of data samples that best represent the overall data space while accurately capturing the decision boundaries... (read more)

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