Model-based machine learning.
Model-based machine learning.
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DOI：
10.1098/rsta.2012.0222
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发表时间：
2013-02-13
期刊：
影响因子：
--
通讯作者：
Bishop CM
中科院分区：
文献类型：
--
作者：
Bishop CM
机器学习领域几十年的研究已经产生了许多不同的算法来解决广泛的问题。为了解决一个新的应用程序，研究人员通常会尝试将他们的问题映射到这些现有方法中的一种，这通常会受到他们对特定算法的熟悉程度以及相应软件实现的可用性的影响。在这项研究中，我们描述了一种应用机器学习的替代方法，其中为每个新应用制定了定制的解决方案。解决方案通过紧凑的建模语言表达，然后自动生成相应的自定义机器学习代码。这种基于模型的方法提供了几个主要优势，包括有机会为特定场景创建高度定制的模型，以及快速原型设计和一系列替代模型的比较。此外，机器学习领域的新手不必学习大量的传统方法，而是可以将注意力集中在理解单个建模环境上。在这项研究中，我们展示了概率图模型，再加上高效的推理算法，为基于模型的机器学习提供了一个非常灵活的基础，我们概述了这个框架的大规模商业应用，涉及数千万用户。我们还描述了概率编程的概念，作为一个强大的软件环境，基于模型的机器学习，我们讨论了一个特定的概率编程语言称为Infer.NET，这已被广泛用于实际应用。
Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications.
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