如何调整SVM Rank的参数？

Question

我正在使用 SVM Rank，它有多个参数，改变了我得到的结果。是否有一些机制可以调整并获得最佳参数，根据验证集上的最佳结果进行调整？

以下是不同的参数：

Learning Options:
     -c float    -> C: trade-off between training error
                    and margin (default 0.01)
     -p [1,2]    -> L-norm to use for slack variables. Use 1 for L1-norm,
                    use 2 for squared slacks. (default 1)
     -o [1,2]    -> Rescaling method to use for loss.
                    1: slack rescaling
                    2: margin rescaling
                    (default 2)
     -l [0..]    -> Loss function to use.
                    0: zero/one loss
                    ?: see below in application specific options
                    (default 1)
Optimization Options (see [2][5]):
     -w [0,..,9] -> choice of structural learning algorithm (default 3):
                    0: n-slack algorithm described in [2]
                    1: n-slack algorithm with shrinking heuristic
                    2: 1-slack algorithm (primal) described in [5]
                    3: 1-slack algorithm (dual) described in [5]
                    4: 1-slack algorithm (dual) with constraint cache [5]
                    9: custom algorithm in svm_struct_learn_custom.c
     -e float    -> epsilon: allow that tolerance for termination
                    criterion (default 0.001000)
     -k [1..]    -> number of new constraints to accumulate before
                    recomputing the QP solution (default 100)
                    (-w 0 and 1 only)
     -f [5..]    -> number of constraints to cache for each example
                    (default 5) (used with -w 4)
     -b [1..100] -> percentage of training set for which to refresh cache
                    when no epsilon violated constraint can be constructed
                    from current cache (default 100%) (used with -w 4)
SVM-light Options for Solving QP Subproblems (see [3]):
     -n [2..q]   -> number of new variables entering the working set
                    in each svm-light iteration (default n = q).
                    Set n < q to prevent zig-zagging.
     -m [5..]    -> size of svm-light cache for kernel evaluations in MB
                    (default 40) (used only for -w 1 with kernels)
     -h [5..]    -> number of svm-light iterations a variable needs to be
                    optimal before considered for shrinking (default 100)
     -# int      -> terminate svm-light QP subproblem optimization, if no
                    progress after this number of iterations.
                    (default 100000)
Kernel Options:
     -t int      -> type of kernel function:
                    0: linear (default)
                    1: polynomial (s a*b+c)^d
                    2: radial basis function exp(-gamma ||a-b||^2)
                    3: sigmoid tanh(s a*b + c)
                    4: user defined kernel from kernel.h
     -d int      -> parameter d in polynomial kernel
     -g float    -> parameter gamma in rbf kernel
     -s float    -> parameter s in sigmoid/poly kernel
     -r float    -> parameter c in sigmoid/poly kernel
     -u string   -> parameter of user defined kernel

Answer 1

这被称为 grid search. I don't know if you're familiar with python and scikit-learn, but either way, I think their description and examples 非常好并且与语言无关。

基本上，您为每个参数指定一些您感兴趣的值（或从中抽取随机样本的间隔，请参阅随机搜索），然后对每个设置组合进行交叉验证（通常 k fold cross validation) 用于计算模型对这些设置的处理情况。表现最好的组合是 returned（scikit-learn 实际上可以 return 组合的排名）。

请注意，这可能需要很长时间。根据您的问题，您应该自己确定一些参数。例如，对于文本分类，您应该只选择线性内核，对于您可能需要 rbf 等的其他问题，不要只将所有内容都扔到网格搜索中，决定使用尽可能多的参数你对算法和手头问题的了解。

如何调整SVM Rank的参数？

How to tune parameters of SVM Rank?

kernel

machine-learning

ranking

svm