选择c和gamma值

问题描述

我正在使用SMO进行SVM分类，我的内核是RBF，现在我想使用网格搜索和交叉验证来选择 c 和 sigma 值，我是内核功能的新手，请逐步进行帮助

Hi I am performing SVM classification using SMO, in which my kernel is RBF, now I want to select c and sigma values, using grid search and cross validation, I am new to kernel functions, please help, in step by step process

推荐答案

1. 选择一些您认为有趣的C和sigma值.例如，C = {1，10，100，1000}和sigma = {.01，.1，1}(我只是在弥补这些问题).
2. 将训练集划分为 k 个(例如10个)部分，最好在分层方式.
3. 遍历所有对C和sigma值.

1. Pick some values for C and sigma that you think are interesting. E.g., C = {1, 10, 100, 1000} and sigma = {.01, .1, 1} (I'm just making these up).
2. Divide the training set into k (e.g. 10) parts, preferably in a stratified way.
3. Loop over all pairs of C and sigma values.

1. 遍历训练集的所有 k 部分.保留第 k 个部分.在所有其他结合的零件上训练分类器，然后对伸出的零件进行测试.
2. 跟踪某些分数(准确性，F1或您要优化的任何分数).

1. Loop over all k parts of your training set. Hold the k'th part out. Train a classifier on all of the other parts combined, then test on the held out part.
2. Keep track of some score (accuracy, F1, or whatever you want to optimize).

通过您刚刚计算出的分数返回C的最佳性能值对，即sigma.

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