A=PLATT(C,H) returns a platt object initialized with hyperparameters H and
based on algorithm C.
Converts a real valued margin producing pattern recognition
algorithm into a conditional probability estimator. It thus
requires a pattern recognition approach that can produce a real
valued output before thresholding, e.g SVMs. It achieves this
by finding the coefficients of a sigmoid by using cross validation.
** Hyperparameters, and their defaults
**
child = svm -- real valued margin algorithm to be used
folds = 3 -- number of cv folds
** Model:
** A --
B -- Parameters of the sigmoid: 1/(1+exp(A*f(x)+B)
** Methods:
** training, testing
** Example:
** [rr,a]=train(platt(svm),gen(toy))
rr.X contains now the probability of being class 2
predictions
[sign(rr.X-0.5),rr.Y]