论文标题
算法搜索中的分解可能性指标的概率
Decomposable Probability-of-Success Metrics in Algorithmic Search
论文作者
论文摘要
先前的研究已在算法搜索框架内使用特定的成功指标来证明机器学习不可能的结果。但是，这种特定的成功指标使我们无法将这些结果应用于其他形式的机器学习，例如转移学习。我们将可分解的指标定义为搜索问题的成功指标类别，这些指标可以表示为解决此问题的概率分布上的线性操作。我们使用任意分解指标来衡量搜索的成功，我们演示了以各种方式绑定成功的定理，从而概括了文献中的几种现有结果。
Previous studies have used a specific success metric within an algorithmic search framework to prove machine learning impossibility results. However, this specific success metric prevents us from applying these results on other forms of machine learning, e.g. transfer learning. We define decomposable metrics as a category of success metrics for search problems which can be expressed as a linear operation on a probability distribution to solve this issue. Using an arbitrary decomposable metric to measure the success of a search, we demonstrate theorems which bound success in various ways, generalizing several existing results in the literature.
