Pool-Based 指无标记数据池直接被给出,主动学习算法在无标记数据池中选择部分数据进行标记。Batch指的是我们往往指定一个batch的数据进行标注,在利用这个batch的数据对模型更新后再次选择新的batch。Pool-Based Batch Active Learning模式也符合目前较为流行的深度学习范式。 考虑基于样本集合S得到的模型fS^与ground-truth...
The goal of pool-based active learning is to choose the best input points to gather output values from a 'pool' of input samples. We develop two pool-based active learning criteria for linear regression. The first criterion allows us to obtain a closed-form solution so it is computationally...
首先,作者们考虑了 pool-based batch active learning for noisy linear models。(即 f∗ 是一个线性模型,而且每一个数据点的 label 都是带噪声的),并提出了一套非常高效的算法,该算法仍然是基于 pseudo-label,即在不知道一个数据点的 label之前用当前模型给它安排的一个假label,在作者们的新算法中,每一个...
摘要: CiteSeerX - Scientific documents that cite the following paper: Employing EM in Pool-Based Active Learning for Text Classification会议名称: Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998), Madison, Wisconsin, USA, July 24-27, 1998 ...
It splits a fully-labeled dataset and remove some label from dataset to simulate the pool-based active learning scenario. Each query of an unlabeled dataset is then equivalent to revealing one labeled example in the original data set. label_digits : This example shows how to use libact in ...
The objective of pool-based incremental active learning is to choose a sample to label from a pool of unlabeled samples in an incremental manner so that the generalization error is minimized. In this scenario, the generalization error often hits a minimum in the middle of the incremental active...