Conformal Predictive Distributions with Kernels
| Autor: | Vladimir Vovk, Ilia Nouretdinov, Alexander Gammerman, Valery Manokhin |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry Bayesian probability Conformal map 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Automation law.invention 010104 statistics & probability Kernel method Development (topology) law 0202 electrical engineering electronic engineering information engineering Fiducial inference 020201 artificial intelligence & image processing Artificial intelligence 0101 mathematics business computer Remote control Parametric statistics |
| Zdroj: | Braverman Readings in Machine Learning. Key Ideas from Inception to Current State ISBN: 9783319994918 Braverman Readings in Machine Learning |
| DOI: | 10.1007/978-3-319-99492-5_4 |
| Popis: | This paper reviews the checkered history of predictive distributions in statistics and discusses two developments, one from recent literature and the other new. The first development is bringing predictive distributions into machine learning, whose early development was so deeply influenced by two remarkable groups at the Institute of Automation and Remote Control. As result, they become more robust and their validity ceases to depend on Bayesian or narrow parametric assumptions. The second development is combining predictive distributions with kernel methods, which were originated by one of those groups, including Emmanuel Braverman. As result, they become more flexible and, therefore, their predictive efficiency improves significantly for realistic non-linear data sets. |
| Databáze: | OpenAIRE |
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