支持向量机增量学习中模型参数选择问题研究

支持向量机性能主要受模型参数的影响,而支持向量机增量学习中模型参数选择问题研究较少。针对这一问题,提出一种支持向量机增量学习中模型参数选择方法。将鲁棒度作为增量学习的性能估计准则,用拟合误差和比例系数调节解空间取值范围,采用梯度下降法搜索参数,用初始模型参数作为梯度下降法的初始值。用该方法对Logistic模型和航空发动机振动监控进行实验。结果表明：与基本遗传算法和梯度法进行比较,所提方法能充分利用历史学习的结果,缩小解空间的搜索范围,加快收敛速度。

Parameter Selection of Support Vector Machine Based Incremental Learning Method

The performance of Support vector machine (SVM) is affected mainly by model parameters, but there is no special method for the model parameter selection of SVM based incremental learning. A new method is proposed in this paper, i.e. taking robustness as criteria for performance evaluation of incremented learning, the range of solution space is designed by fitting error and scale factor, then the gradient descent algorithm is used to search the parameters. The experiments with this new method are made on the Logistic model regressing and aero engine vibration monitoring, and the comparison of this new method with the genetic algorithm and the gradient descent algorithm is made. The result indicates that the use of the proposed method can take full advantage of the results of historical learning, thus the solution space is narrowed, and iteration steps are reduced.