论文标题
与概率法发现的机器学习发现：简洁的简介
Machine Learning with Probabilistic Law Discovery: A Concise Introduction
论文作者
论文摘要
概率法律发现（PLD）是一种基于逻辑的机器学习方法，它实现了概率规则学习的变体。在某些方面，PLD接近决策树/随机森林方法，但在定义相关规则方面有很大不同。 PLD的学习过程解决了与搜索规则（称为概率定律）有关的优化问题，该问题的长度最小且概率相对较高。在推论时，这些规则的集合用于预测。概率定律是人类可读的，基于PLD的模型是透明的，可以固有地解释。 PLD的应用包括分类/聚类/回归任务，以及时间序列分析/异常检测和自适应（机器人）控制。在本文中，我们概述了PLD的主要原则，强调其收益和局限性，并提供一些申请准则。
Probabilistic Law Discovery (PLD) is a logic based Machine Learning method, which implements a variant of probabilistic rule learning. In several aspects, PLD is close to Decision Tree/Random Forest methods, but it differs significantly in how relevant rules are defined. The learning procedure of PLD solves the optimization problem related to the search for rules (called probabilistic laws), which have a minimal length and relatively high probability. At inference, ensembles of these rules are used for prediction. Probabilistic laws are human-readable and PLD based models are transparent and inherently interpretable. Applications of PLD include classification/clusterization/regression tasks, as well as time series analysis/anomaly detection and adaptive (robotic) control. In this paper, we outline the main principles of PLD, highlight its benefits and limitations and provide some application guidelines.
