徐海蛟博士
真實場景下，數據的特徵可能比較複雜，系統提供的4種核函數或許達不到最佳效果，那麼就需要自定義核函數了。當然，有很多大牛幹這個事情，我們可以拿來使用，通過自定義核方式。
如何用？這時候不再把訓練與測試數據文件作爲輸入參數了，而是使用核矩陣作爲輸入參數。
Assume there are L training instances x1, ..., xL . ... L行訓練樣本
Let K(x, y) be the kernel value of two instances x 與 y. The input formats are:
New training instance for xi:
<label> 0:i 1:K(xi,x1) ... L:K(xi,xL)
New testing instance for any x:
<label> 0:? 1:K(x,x1) ... L:K(x,xL)
That is, in the training file the first column must be the "ID" of xi. In testing, ? can be any value.
All kernel values including ZEROs must be explicitly provided. Any permutation or random subsets of the training/testing files are also valid (see examples below).
Note: the format is slightly different from the precomputed kernel
package released in libsvmtools earlier.
例子:
Assume the original training data has 3個four-feature instances, testing data has one instance:
15 1:1 2:1 3:1 4:1
45 2:3 4:3
25 3:1
-----------------------------------
15 1:1 3:1
若使用線性核, we have the following new training/testing sets:
15 0:1 1:4 2:6 3:1
45 0:2 1:6 2:18 3:0
25 0:3 1:1 2:0 3:1
-------------------------------------
15 0:? 1:2 2:0 3:1
? can be any value.
Any subset of the above training file is also valid. 例如,
25 0:3 1:1 2:0 3:1
45 0:2 1:6 2:18 3:0
意味着核矩陣是:
[K(2,2) K(2,3)] = [18 0]
[K(3,2) K(3,3)] = [0 1]