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1、香港旅游需求预测的稀疏高斯过程回归模型摘要 近年来，高斯过程（GP）模型已被广泛研究，努力解决机器学习问题。能 够灵活的使用默瑟内核和概率推理的贝叶斯框架的非参数化建模能力的模型是 很重要的。在本文中,我们提出一个稀疏 GP 回归(GPR)在香港旅游需求预测模型。 探地雷达的 sparsification 过程模型不仅降低了计算复杂度,而且提高了泛化 能力。我们实验所提出的模型是适用于香港的旅游业月度需求数据，以及稀疏 GPR 模型的性能与各种基于内核的模式进行比较，以显示其有效性。稀疏 GPR 模型建议显示，其预测能力优于那些 ARMA 模型和国家的最先进的两种 SVM 模型。1.引言 在国
2、际入境旅游需求方面，无论是在旅游收入和旅游人数上，香港旅游业 最近都发生了剧烈的变化。与这些翻天覆地的变化相关的是入境旅游市场的结 构调整,因此对旅游产品和服务的需求不同。作为一个例子，对个人游计划在中 国内地某城市居民推出后在香港旅游需求的变化更为突出(Law, To, Law, 2000b; Law, Goh, Mok, 1990; Witt Quinonero-Candela Law, 2000a; Uysal Law,2000b; Law Fan, Chen, Guo Xu, Law, Xu Kim Lathiras Lee, Var, Lim, 1997; Song 香港理工大学（G
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