| 书目名称 | Learning in the Absence of Training Data | | 编辑 | Dalia Chakrabarty | | 视频video | | | 概述 | Describes a new reliable forecasting technique that works by learning the evolution-driving function.Presents a way of comparing two disparately-long time series datasets via a distance between graphs | | 图书封面 |  | | 描述 | .This book introduces the concept of “bespoke learning”, a new mechanistic approach that makes it possible to generate values of an output variable at each designated value of an associated input variable. Here the output variable generally provides information about the system’s behaviour/structure, and the aim is to learn the input-output relationship, even though little to no information on the output is available, as in multiple real-world problems. Once the output values have been bespoke-learnt, the originally-absent training set of input-output pairs becomes available, so that (supervised) learning of the sought inter-variable relation is then possible. Three ways of undertaking such bespoke learning are offered: by tapping into system dynamics in generic dynamical systems, to learn the function that causes the system’s evolution; by comparing realisations of a random graph variable, given multivariate time series datasets of disparate temporal coverage; and by designing maximally information-availing likelihoods in static systems. These methodologies are applied to four different real-world problems: forecasting daily COVID-19 infection numbers; learning the gravitational m | | 出版日期 | Book 2023 | | 关键词 | Supervised Learning; Training Data; Prediction given Test Data; Bayesian methods; Choosing priors on unk | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-31011-9 | | isbn_softcover | 978-3-031-31013-3 | | isbn_ebook | 978-3-031-31011-9 | | copyright | Springer Nature Switzerland AG 2023 |
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发表于 2025-3-21 16:20:57
