I'm doing Gaussian Processes for Regression using GPML toolbox. However, after optimization using 'minimize.m' (without mean functions), I get some negative hyper-parameters! 

The initial hyper-parameters are: 

`hyp.cov = [0; 0]; % two hyper-parameters in covariance kernel (length-scale & amplitute)
hyp.lik = log(0.1); %hyper-parameters of noise`

The original training data:

```
x=[819 1119 1419 1599 1719 1839 1899 2019 2079 2139];   %coordinates

y=[105.00  114.33  126.33 130.33  116.33  103.00  103.00  124.67  122.67  109.00]; %training data

```

In my codes, y are **NORMALIZED** to have zero mean and unit variance. Then optimize:

```
hyp = minimize(hyp, @gp, -100, @infExact, [], {@covSEiso}, likfunc, x,y);

```

After about 100 iterations, I get some negative hyper-parameters!!! It is quite confusing....

However, if I don't normalize y, all hyper-parameters will be positive after optimization.

Could anyone tell me what does Negative Hyper-parameters mean? Should I normalize the data?