Semi-supervised Kernel Logistic Regression and Its Extension to Active Learning Based on A-Optimality
Semi-supervised Kernel Logistic Regression and Its Extension to Active Learning Based on A-Optimality
复制标题
DOI：
10.1109/icdmw.2007.64
复制
发表时间：
2007-10
期刊：
影响因子：
--
通讯作者：
Shuxin Li;Robert Lee;S. Lang
中科院分区：
文献类型：
--
作者：
Shuxin Li;Robert Lee;S. Lang
本文的目的是介绍新的方法，核逻辑回归（KLR）在半监督设置。利用Laplacian核矩阵的特殊结构，我们提出了新的配方，有效地减少负对数似然的KLR模型。此外，我们提出了新的算法基于池的主动学习的基础上A-最优性，其中半监督KLR是用来估计类概率。我们证明了主动学习算法可以在由相关核矩阵定义的特征空间中进行。我们给出的实验结果表明，所提出的主动学习方法生成准确的分类器使用较少的标记数据点的随机查询相比。
The purpose of this paper is to introduce new approaches for kernel logistic regression (KLR) in a semi-supervised setting. Using the special structure of Laplacian kernel matrices, we propose new formulations which minimize the negative log likelihood of the KLR model efficiently. Also, we propose new algorithms for pool-based active learning based on A-optimality in which the semi-supervised KLR is used to estimate the class probabilities. We show that the active learning algorithms can be carried out in the fea- ture space defined by the associated kernel matrices. We give experimental results showing that the proposed active learning method generate accurate classifiers using a fewer number of labeled data points compared with the random queries.