训练支持向量机模型时如何调整参数

这里的亮点 blog on the kernel width choice :

To pick, say 1000 pairs (x,x’) at random from your dataset, compute the distance
of all such pairs and take the median, the 0.1 and the 0.9 quantile. Now pick λ 
to be the inverse any of these three numbers. With a little bit of cross 
validation you will figure out which one of the three is best. In most cases you 
won’t need to search any further.

和 this post 通过交叉验证,分析了这种方法有效的原因。基本上避免了为所有数据点或仅为一个数据点更改决策函数。

此外,您可以在SVM中搜索关于参数选择的“启发式方法”。例如,在 M.Boardman et al's A Heuristic for Free Parameter Optimization with Support Vector Machines ,作者应用 simulated annealing 以与穷举网格搜索相比提高参数搜索效率。