拟牛顿算法在SVM内核优化中的应用

使用SVM进行分类，超参数的选择非常重要，它直接影响分类的性能。在实际应用中，最优SVM算法参数选择还只能是凭借经验、实验对比、大范围的搜寻或者利用软件包提供的交叉确认功能进行寻优。而拟牛顿算法，可在一个校验集上最小化一个经验误差估计来优化SVM的超参数，使超参数在分类任务中达到最优值，从而取得理想的分类结果。该文对拟牛顿算法进行了探讨，并将其应用在基于SVM的羽绒识别系统中，实验结果表明，该算法是有效的，与未经过超参数优化的SVM分类器相比，羽绒的识别率有了较大提高。

Application of Quasi-Newton Method in Kernel Parameters Optimization of SVM

Support vector machines(SVM) are efficient in a large number of real-world applications.However,the classification results highly depend on the parameters of the model.For any SVM classification task,the best values for these parameters are usually picked by experience,experiment compare and large-scale search,or using cross validation providing by software package to optimize.For optimizing SVM hyper-parameters,this optimization scheme minimizes an empirical error estimate using a quasi-Newton optimization method on the validation set.The method shows satisfactory results in feather and down category recognition.