论文标题
通过高斯近似值，有效的基于图的主动学习具有概率的可能性
Efficient Graph-Based Active Learning with Probit Likelihood via Gaussian Approximations
论文作者
论文摘要
我们提出了一种在非高斯贝叶斯模型下进行积极学习对基于图的半监督学习（SSL）的新颖适应。我们提出了非高斯分布的近似值，以使以前基于高斯的采集功能适应这些更一般的情况。我们开发了一个有效的排名更新，以应用基于“浏览”的方法以及模型再培训。我们还基于这些近似值引入了一种新颖的“模型更改”采集功能，该近似值进一步扩展了此类方法的主动学习采集功能的可用收集。
We present a novel adaptation of active learning to graph-based semi-supervised learning (SSL) under non-Gaussian Bayesian models. We present an approximation of non-Gaussian distributions to adapt previously Gaussian-based acquisition functions to these more general cases. We develop an efficient rank-one update for applying "look-ahead" based methods as well as model retraining. We also introduce a novel "model change" acquisition function based on these approximations that further expands the available collection of active learning acquisition functions for such methods.
