论文标题
如何调整RBF SVM超参数？：对18搜索算法的经验评估
How to tune the RBF SVM hyperparameters?: An empirical evaluation of 18 search algorithms
论文作者
论文摘要
具有RBF内核的SVM通常是大多数数据集的最佳分类算法之一，但是将两个超参数$ C $和$γ$调整为数据本身很重要。通常，超参数的选择是一个非凸优化问题，因此已经提出了许多算法来解决它：网格搜索，随机搜索，贝叶斯优化，模拟退火，粒子群优化，尼尔德·米德等。还提出了将$γ$和$ c $选择的选择。我们从经验上比较了这些提出的搜索算法中的18种（具有不同的参数化，总共47个组合）在115个现实生活中的二进制数据集上。我们发现（除其他事项外），parzen估计量和粒子群优化的树木选择更好的超参数，而相对于网格搜索的计算时间仅略有增加，并具有相同数量的评估。我们还发现，花费太多的计算努力搜索超参数不会为将来的数据带来更好的性能，并且不同过程之间没有显着差异来选择最佳的超参数集当搜索算法发现多个以上时。
SVM with an RBF kernel is usually one of the best classification algorithms for most data sets, but it is important to tune the two hyperparameters $C$ and $γ$ to the data itself. In general, the selection of the hyperparameters is a non-convex optimization problem and thus many algorithms have been proposed to solve it, among them: grid search, random search, Bayesian optimization, simulated annealing, particle swarm optimization, Nelder Mead, and others. There have also been proposals to decouple the selection of $γ$ and $C$. We empirically compare 18 of these proposed search algorithms (with different parameterizations for a total of 47 combinations) on 115 real-life binary data sets. We find (among other things) that trees of Parzen estimators and particle swarm optimization select better hyperparameters with only a slight increase in computation time with respect to a grid search with the same number of evaluations. We also find that spending too much computational effort searching the hyperparameters will not likely result in better performance for future data and that there are no significant differences among the different procedures to select the best set of hyperparameters when more than one is found by the search algorithms.
