7. The method of claim 4 wherein said SVM classifier is based on a formulation having two cost factors (one for a positive class, one for a negative class), as follows: min [ 1 2 ⁢ ∑ i ⁢ ∑ j ⁢ α i ⁢ α j ⁢ y i ⁢ y j ⁢ K ⁡ ( x i , x j ) - ∑ i ⁢ α i ] where: α i is a Lagrange multiplier for example x i , y i = { + 1 , iff ⁢ ⁢ x i ⁢ ⁢ is ⁢ ⁢ in ⁢ ⁢ the ⁢ ⁢ positive ⁢ ⁢ class - 1 , iff ⁢ ⁢ x i ⁢ ⁢ is ⁢ ⁢ in ⁢ ⁢ the ⁢ ⁢ negative ⁢ ⁢ class , Φ(x) is a function that maps input vectors to feature vectors, 
 K ( x i , x j )=Φ (xi) ·Φ (xj) Subject to the constraints: 0 ≤ α i ≤ C + , ∀ { i | y i = + 1 } 0 ≤ α i ≤ C - , ∀ { i | y i = - 1 } ∑ i ⁢ y i ⁢ α i = 0 where C + and C − are the two user definable cost factors.
4. The method of claim 1 wherein said at least one tunable parameter of said SVM classifier is optimized using a method comprising the steps of: allocating a subset of the training examples assigned to said first category to a “holdout” set, wherein said subset of training examples are left out of said training step.