SVO

Support Vector Optimiser, low-level routine

    [V,J,C,NU] = SVO(K,NLAB,C,OPTIONS)

Input
K	Similarity matrix
NLAB	Label list consisting of -1/+1
C	Scalar for weighting the errors (optional; default: 1)
OPTIONS
.PD_CHECK	force positive definiteness of the kernel by adding a small constant  to a kernel diagonal (default: 1)
.BIAS_IN_ADMREG	it may happen that bias of svc (b term) is not defined, then  if BIAS_IN_ADMREG == 1, b will be taken from the midpoint of its admissible  region, otherwise (BIAS_IN_ADMREG == 0) the situation will be considered  as an optimisation failure and treated accordingly (deafault: 1)
.PF_ON_FAILURE	if optimisation is failed (or bias is undefined and BIAS_IN_ADMREG is 0) and PF_ON_FAILURE == 1, then Pseudo Fisher classifier will be computed,  otherwise (PF_ON_FAILURE == 0) an error will be issued (default: 1)

Output
V	Vector of weights for the support vectors
J	Index vector pointing to the support vectors
C	C which was actually used for optimisation
NU	NU parameter of NUSVC algorithm, which gives the same classifier

Description

A low level routine that optimises the set of support vectors for a 2-class  classification problem based on the similarity matrix K computed from the  training set. SVO is called directly from SVC. The labels NLAB should indicate  the two classes by +1 and -1. Optimisation is done by a quadratic programming.  If available, the QLD function is used, otherwise an appropriate Matlab routine.

See also

svc,