标题： 组合建模与预测市场的学习 显示英文标题

标题： Combinatorial Modelling and Learning with Prediction Markets

摘要： 将适当的模型以合适的方式结合以实现高性能在当今机器学习领域中很常见。 尽管已经创建了大量的组合模型，但很少有人关注不同模型之间的共性及其联系。 因此，研究一种通用的建模技术是有价值的，以便深入理解模型组合，并为创建新模型提供启示。 预测市场显示出成为这种通用、灵活的组合模型的潜力。 通过回顾几种流行的组合模型和预测市场模型，本文旨在说明市场模型如何概括不同的组合结构，以及它们在特定条件下如何实现这些流行的组合模型。 此外，我们将看到在不同的市场模型中，Storkey 的\emph{机器学习市场}提供了比其他模型更基础、更通用的建模机制，并且在理论研究和应用方面都具有显著的吸引力。 显示英文摘要

摘要： Combining models in appropriate ways to achieve high performance is commonly seen in machine learning fields today. Although a large amount of combinatorial models have been created, little attention is drawn to the commons in different models and their connections. A general modelling technique is thus worth studying to understand model combination deeply and shed light on creating new models. Prediction markets show a promise of becoming such a generic, flexible combinatorial model. By reviewing on several popular combinatorial models and prediction market models, this paper aims to show how the market models can generalise different combinatorial stuctures and how they implement these popular combinatorial models in specific conditions. Besides, we will see among different market models, Storkey's \emph{Machine Learning Markets} provide more fundamental, generic modelling mechanisms than the others, and it has a significant appeal for both theoretical study and application.