货币危机判别的支持向量机模型
Support Vector Machine Model for Currency Crisis Discrimination
摘要：
支持向量机是一种基于结构风险最小化思想的强有力的分类技术。核函数的使用使得能够解决维数灾难。然而，对于某些问题，合适的核函数依赖于特定的数据集，因此没有很好的方法来选择核函数。本文利用支持向量机建立阿根廷货币危机的实证模型。通过在真实数据集上训练模型，开发了一种估计技术，该技术提供了合理准确的模型输出，并帮助政策制定者识别可能发生货币危机的情况。三阶和四阶多项式核通常是实现分类器高泛化性能的最佳选择。支持向量机具有算法简单、易于实现、能容忍维数灾难和良好的经验性能等特点，成熟度较高。令人满意的结果表明，货币危机的情况是适当的模拟，只有一小部分的数据库，可以作为一个评估工具，以及早期预警系统。据了解，这是支持向量机方法对阿根廷货币危机评估的第一项工作。
英文摘要：
Support Vector Machine (SVM) is powerful classification technique based on the idea of structural risk minimization. Use of kernel function enables curse of dimensionality to be addressed. However, proper kernel function for certain problem is dependent on specific dataset and as such there is no good method on choice of kernel function. In this paper, SVM is used to build empirical models of currency crisis in Argentina. An estimation technique is developed by training model on real life data set which provides reasonably accurate model outputs and helps policy makers to identify situations in which currency crisis may happen. The third and fourth order polynomial kernel is generally best choice to achieve high generalization of classifier performance. SVM has high level of maturity with algorithms that are simple, easy to implement, tolerates curse of dimensionality and good empirical performance. The satisfactory results show that currency crisis situation is properly emulated using only small fraction of database and could be used as an evaluation tool as well as an early warning system. To the best of knowledge this is the first work on SVM approach for currency crisis evaluation of Argentina.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
