Machine learning is a field composed of various pillars. Traditionally, supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL) are the dominating learning paradigms that inspired the field since the 1950s. Based on these, thousands of different methods have been developed during the last seven decades used in nearly all application domains. However, recently, other learning paradigms are gaining momentum which complement and extend the above learning paradigms significantly. These are multi-label learning (MLL), semi-supervised learning (SSL), one-class classification (OCC), positive-unlabeled learning (PUL), transfer learning (TL), multi-task learning (MTL), and one-shot learning (OSL). The purpose of this article is a systematic discussion of these modern learning paradigms and their connection to the traditional ones. We discuss each of the learning paradigms formally by defining key constituents and paying particular attention to the data requirements for allowing an easy connection to applications. That means, we assume a data-driven perspective. This perspective will also allow a systematic identification of relations between the individual learning paradigms in the form of a learning-paradigm graph (LP-graph). Overall, the LP-graph establishes a taxonomy among 10 different learning paradigms.

中文翻译：

---

机器学习范式分类：以数据为中心的视角

机器学习是一个由各种支柱组成的领域。传统上，监督学习 (SL)、无监督学习 (UL) 和强化学习 (RL) 是自 1950 年代以来启发该领域的主要学习范式。基于这些，在过去的七十年中，已经开发了数千种不同的方法，用于几乎所有的应用领域。然而，最近，其他学习范式正在获得动力，它们显着补充和扩展了上述学习范式。它们是多标签学习 (MLL)、半监督学习 (SSL)、一类分类 (OCC)、正无标签学习 (PUL)、迁移学习 (TL)、多任务学习 (MTL) 和一镜头学习（OSL）。本文的目的是系统地讨论这些现代学习范式及其与传统学习范式的联系。我们通过定义关键组成部分并特别关注数据要求以允许轻松连接到应用程序，从而正式讨论每个学习范式。这意味着，我们假设一个数据驱动的观点。这种观点还将允许以学习范式图（LP-图）的形式系统地识别个体学习范式之间的关系。总体而言，LP-graph 在 10 种不同的学习范式中建立了分类。我们假设一个数据驱动的观点。这种观点还将允许以学习范式图（LP-图）的形式系统地识别个体学习范式之间的关系。总体而言，LP-graph 在 10 种不同的学习范式中建立了分类。我们假设一个数据驱动的观点。这种观点还将允许以学习范式图（LP-图）的形式系统地识别个体学习范式之间的关系。总体而言，LP-graph 在 10 种不同的学习范式中建立了分类。