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首页> 外文期刊>Journal of Hydroinformatics >Using a multi-objective genetic algorithm for SVM construction
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Using a multi-objective genetic algorithm for SVM construction

机译：使用多目标遗传算法构建SVM

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摘要

Support Vector Machines are kernel machines useful for classification and regression problems. In this paper, they are used for non-linear regression of environmental data. From a structural point of view, Support vector Machines are particular Artificial Neural Networks and their training paradigm has some positive implications. In fact, the original training approach is useful to overcome the curse of dimensionality and too strict assumptions on statistics of the errors in data. Support Vector Machines and Radial Basis Function Regularised Networks are presented within a common structural framework for non-linear regression in order to emphasise the training strategy for support vector machines and to better explain the multi-objective approach in support vector machines' construction. A support vector machine's performance depends on the kernel parameter, input selection and ε-tube optimal dimension. These will be used as decision variables for the evolutionary strategy based on a Genetic Algorithm, which exhibits the number of support vectors, for the capacity of machine, and the fitness to a validation subset, for the model accuracy in mapping the underlying physical phenomena, as objective functions. The strategy is tested on a case study dealing with groundwater modelling, based on time series (past measured rainfalls and levels) for level predictions at variable time horizons.
机译：支持向量机是用于分类和回归问题的内核机器。在本文中，它们被用于环境数据的非线性回归。从结构的角度来看，支持向量机是特定的人工神经网络，其训练范式具有某些积极意义。实际上，原始的训练方法对于克服维数的诅咒和对数据错误的统计数据的过于严格的假设很有用。支持向量机和径向基函数正则化网络在用于非线性回归的通用结构框架中提出，以强调支持向量机的训练策略并更好地解释支持向量机构造中的多目标方法。支持向量机的性能取决于内核参数，输入选择和ε管最佳尺寸。这些将用作基于遗传算法的进化策略的决策变量，该遗传算法展示支持向量的数量，机器的容量以及对验证子集的适合度，以及在映射潜在物理现象时的模型准确性，作为目标函数。该策略已在处理地下水建模的案例研究中进行了测试，该案例基于时间序列（过去测得的降雨量和水位），用于在不同的时间范围内进行水位预测。

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