JSVMLearner with non linear kernel

Has this not been dealt with before?

http://rapid-i.com/rapidforum/index.php/topic,1455.msg5535.html#msg5535

thanks, but it's a different problem,

ilaria

Hi,
I don't think haddock referred to the question in this topic. I rather believe he points to the solution I recommended: Reading the original paper.

Greetings,
  Sebastian

thanks, but I already read the paper and in fact I learnt that the vector w should be calculated in another way, that is with phi(x_i) and not with the x_i, as Rapid Miner does.

ilaria

Hi,
that's correct. So where did you see, that w was calculated in the wrong way? Did you see it in the source code or was it displayed within rapid miner?

Greetings,
  Sebastian

Hi,
I simply calculated it by myself, in order to see if I had understood the algorithm, and I remarked that Rapid Miner calculates it with the x_i and not with the phi(x_i).  It's not a problem if you use only the confidence as output, but I thought It was interesting to point it out,

ilaria

G'Day Folks,

Not sure Ilaria got due credit here for the close work, so let me be the first to commend it to you, top stuff!

Hi again,
where exactly is the w shown in RapidMiner? I just want to take a closer look. If I remember correctly, the phi cannot be expressed for any kernel?

Greetings,
  Sebastian

Hi, you are right, the phi cannot be expressed for all kernels. Probably RM could simply not to give the w, which now is given in the training process output model, because it's not necessary.
greetings,

ilaria