论文标题
基于池基于多内核的顺序积极学习
Pool-based sequential active learning with multi kernels
论文作者
论文摘要
我们研究了基于池的顺序学习（AL），其中每次根据选择标准从大量未标记的数据中查询一个样本。在此框架中，我们提出了两个选择标准，即通过利用多种内核学习（MKL）的特定结构来称为预期内智库（EKD）和预期内内尼尔 - 损失（EKL）。同样，据确定，所提出的EKD和EKL成功地将流行查询的概念概念（QBC）和预期模型更改（EMC）分别推广。通过实验室的实验结果，我们与现有方法相比验证了所提出标准的有效性。
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. For this framework, we propose two selection criteria, named expected-kernel-discrepancy (EKD) and expected-kernel-loss (EKL), by leveraging the particular structure of multiple kernel learning (MKL). Also, it is identified that the proposed EKD and EKL successfully generalize the concepts of popular query-by-committee (QBC) and expected-model-change (EMC), respectively. Via experimental results with real-data sets, we verify the effectiveness of the proposed criteria compared with the existing methods.
