Semi-Supervised Logistic Discrimination via Regularized Gaussian Basis Expansions
Semi-Supervised Logistic Discrimination via Regularized Gaussian Basis Expansions
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DOI：
10.1080/03610926.2010.481370
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发表时间：
2011-04
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影响因子：
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通讯作者：
Shuichi Kawano;S. Konishi
中科院分区：
文献类型：
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作者：
Shuichi Kawano;S. Konishi
针对复杂结构数据的分类问题，提出了一种基于标记数据集和未标记数据集的分类方法。我们介绍了一个半监督逻辑判别模型与高斯基扩展。采用正则化方法沿着EM算法对Logistic模型中的未知参数进行估计。对于调整参数的选择，我们从贝叶斯的观点推导出一个模型选择标准。数值研究进行调查我们提出的建模程序的有效性。
The problem of constructing classification methods based on both labeled and unlabeled data sets is considered for analyzing data with complex structures. We introduce a semi-supervised logistic discriminant model with Gaussian basis expansions. Unknown parameters included in the logistic model are estimated by regularization method along with the technique of EM algorithm. For selection of adjusted parameters, we derive a model selection criterion from Bayesian viewpoints. Numerical studies are conducted to investigate the effectiveness of our proposed modeling procedures.