Hi,
If I do a standard svm regression with e1071
x <- seq(0.1, 5, by = 0.05)
y <- log(x) + rnorm(x, sd = 0.2)
m <- svm(x, y)
we can do predict(m,x) to get the fitted values. But what if I wan tho get them
by hand?
Seem to me like it should be
w = t(m$coefs)%*%m$SV
x.scaled = scale(x, m$x.scale[[1]], m$x.scale[[2]])
t(w %*% t(as.matrix(x.scaled))) - m$rho but this is wrong
If i get this right what the answer should be is
f(x) = w%*%phi(x)+b but the question is what exactly is phi (by default) and
how do you do this if you cannot get phi in
closer form (rbf kernel?). It seems like the answer would lie with the dual
representation of the above, i think it is
f(x) = sum_{support vectors}{(a_i-a*^{*}_{i})K(x_{i},x)}+b but how do you get
the a variables everything else. An actual formula would be greatly appreciated!
Thanks!
-Andrei
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