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Nov, 2011
监督学习中的无免费午餐和奥卡姆剃刀原则
No Free Lunch versus Occam's Razor in Supervised Learning
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Tor Lattimore, Marcus Hutter
TL;DR
应用算法信息理论证明普适偏好是成功算法的关键,提出以索洛蒙诺夫归纳为灵感的新离线分类算法,证明随机选择训练数据可有效降低误分类率。
Abstract
The No Free Lunch theorems are often used to argue that domain specific knowledge is required to design successful algorithms. We use
algorithmic information theory
to argue the case for a
universal bias
allowing
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