On extension theorems and their connection to universal consistency in machine learning
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- 作者：Andreas Christmann
- 所属单位：数理与信息工程学院
- 文献类型：期刊
- 发表时间：2016-01-01
- 发表刊物：Analysis and Applications
- 卷号：Vol.14
- 期号：No.6
- 页面范围：795
- Issn号：0219-5305;1793-6861
- 是否译文：否
- 关键字：Machine learning; kernel learning; universal consistency; Dugundji extension theorem; Lusin\'s theorem; denseness; reproducing kernel Hilbert space
- 摘要：Statistical machine learning plays an important role in modern statistics and computer science. One main goal of statistical machine learning is to provide universally consistent algorithms, i.e. the estimator converges in probability or in some stronger
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