Patent ID: 8065241

Claim:
A method for training a support vector machine, the method comprising: receiving input vectors from a labeled dataset that have been clustered into one or more clusters, the input vectors from the labeled dataset relating to a phenomenon of interest; creating simultaneously a t+1 space of a decision function and a t space of a correcting function where t is the number of clusters; specifying generalized constraints in said space of said correcting function which are dependent upon the clusters of input vectors in the dataset; generating in said space of said correcting function, a subset of the input vectors from the dataset using said decision function subject to the generalized constraints, said subset of the input vectors defining a separating hyperplane in a space of said decision function, said subset of the input vectors for use by said support vector machine in describing input vectors from an unlabeled dataset relating to said phenomenon of interest; and wherein the generalized constraints can be represented as ∑ i ∈ T r ⁢ α i ⁢ K r ⁡ ( x i , x j ) ≤ C ⁢ ∑ i ∈ T r ⁢ K r ⁡ ( x i , x j ) , j ∈ T r , r = 1 , … ⁢ , t ∑ i ∈ T r ⁢ α i ≤  T r  ⁢ C , r = 1 , … ⁢ ⁢ t , ⁢ ∑ i = 1 l ⁢ y i ⁢ α i = 0 , α i ≥ 0 where there are t clusters and where K r is the kernel function defined on the correct space into which the input vectors within the cluster r are mapped, α i are the coefficients determining the separating hyperplane in the separating space, and T r defines a set of indices of input vectors within a cluster r.