This weakens the accuracy of the diversity measurement. Secondly, these methods usually exploit the decision boundary by querying the data points close to it.

The proposed deep neural network based algorithm outperforms the baselines with both higher classification accuracy and faster convergence rate on a variety ...

We propose an adaptive BMAL method that uses deep neural networks to learn similarity and balance exploration and exploitation. Exploration strategy represents ...

... A straightforward method is to utilize the distance or similarity between data points in the feature space and avoid selecting similar samples. Yin et al. [ ...

Feb 23, 2024 · This paper proposes micro-MetaStream, a meta-learning based method to recommend the most suitable learning algorithm for each new example ...

A. Bondu, V. Lemaire, and M. Boullé, “Exploration vs. exploitation in active learning : A Bayesian approach,” In The 2010 International Joint Conference on ...

Oct 1, 2022 · In the active learning stage, we use the adaptive algorithm to select the instance by balancing the maximum uncertainty (exploration) and ...

Active learning provides a framework to adaptively query the most informative experiments towards learning an unknown black-box function.

In this paper, we are proposing a unified and prin- cipled method for both the querying and training processes in deep batch active learning. We are.

Deep Similarity-Based Batch Mode Active Learning with Exploration-Exploitation. In IEEE International Conference on Data Mining, ICDM. 575--584. Google ...