Problem with SVM Regression Results

Dear all,

I have tried to do prediction using LibSVM in KNIME. I use RBF kernel and epsilon-SVR, also I tried nu-SVR, as SVM type. 
However, the results seem misleading. Most of predicted values are similar though they come from different attribute values.
In my dataset, I have three nominal attributes. I convert them into numerical since SVM only support this attribute type. I also tried to convert them, using weka, into binary attributes, but there was no change to the final results.
Actually there are two other textual attributes, previously I have included them as numeric one ( I convert into wordVector), but I still got the same results.  
Here is the detailed result:

Can anyone help me to fix this problem?

Thanks in advance.

Hmm, this is hard to diagnose without the data/workflow. But from the little I see, the attributes seem to have heavily different ranges (most are binary and "timest..." has values in the hundredthousands). This will likely cause most kernels to have unsuitable parameters - e.g. RBF kernels with a much too small width). You may want to normalize the data before feeding it into the SVM?

Hope this helps. Michael