15. The method of claim 14 , comprising writing a semi-supervised support vector objective as follows: w * = arg ⁢ ⁢ min w ∈ ⁢ ℝ d , { μ j ∈ { 0 , 1 } } j = 1 u ⁢ λ 2 ⁢  w  2 + 1 2 ⁢ l ⁢ ∑ i = 1 l ⁢ max ⁡ [ 0 , 1 - y i ⁡ ( w T ⁢ x i ) ] 2 + λ ′ 2 ⁢ u ⁢ ∑ j = 1 u ⁢ ( μ j ⁢ ⁢ max ⁢ [ 0 , 1 - ( w T ⁢ x j ′ ) ] 2 + ( 1 - μ j ) ⁢ max ⁡ [ 0 , 1 + ( w T ⁢ x j ′ ) ] 2 ) wherein binary valued variables μ j =(1+y j )/2 are introduced, p j ε[0,1] denote the belief probability that the unlabeled example belongs to the positive class, l is the number of labeled example elements, u is the number of unlabeled example elements, wherein λ and λ′ are regularization parameters, and the Ising model of mean field annealing motivates the following objective function, where the binary variables μ j are relaxed to probability variables p j , and include entropy terms for the distributions defined by p j : w T * = arg ⁢ ⁢ min w ∈ ℝ d , { p j ∈ [ 0 ⁢ ⁢ 1 ] } j = 1 u ⁢ λ 2 ⁢  w  2 + 1 2 ⁢ l ⁢ ∑ i = 1 l ⁢ max ⁡ [ 0 , 1 - y i ⁡ ( w T , x i ) ] 2 + λ ′ 2 ⁢ u ⁢ ∑ j = 1 u ⁢ ( p j ⁢ max ⁡ [ 0 , 1 - ( w T ⁢ x j ′ ) ] 2 + ( 1 - p j ) ⁢ max ⁡ [ 0 , 1 + ( w T ⁢ x j ′ ) ] 2 ) + T 2 ⁢ u ⁢ ∑ j = 1 u ⁢ ( p j ⁢ ⁢ log ⁢ ⁢ p j + ( 1 - p j ) ⁢ ⁢ log ⁢ ⁢ ( 1 - p j ) ) ( 8 ) wherein the temperature T parameterizes a family of objective functions, wherein the objective function for a fixed T is minimized under the following class balancing constraints: 1 u ⁢ ∑ j = 1 u ⁢ p j = r where r, the fraction of the number of unlabeled examples belonging to the positive class.
15. The method of claim 14 , comprising writing a semi-supervised support vector objective as follows: w * = arg ⁢ ⁢ min w ∈ ⁢ ℝ d , { μ j ∈ { 0 , 1 } } j = 1 u ⁢ λ 2 ⁢  w  2 + 1 2 ⁢ l ⁢ ∑ i = 1 l ⁢ max ⁡ [ 0 , 1 - y i ⁡ ( w T ⁢ x i ) ] 2 + λ ′ 2 ⁢ u ⁢ ∑ j = 1 u ⁢ ( μ j ⁢ ⁢ max ⁢ [ 0 , 1 - ( w T ⁢ x j ′ ) ] 2 + ( 1 - μ j ) ⁢ max ⁡ [ 0 , 1 + ( w T ⁢ x j ′ ) ] 2 ) wherein binary valued variables μ j =(1+y j )/2 are introduced, p j ε[0,1] denote the belief probability that the unlabeled example belongs to the positive class, l is the number of labeled example elements, u is the number of unlabeled example elements, wherein λ and λ′ are regularization parameters, and the Ising model of mean field annealing motivates the following objective function, where the binary variables μ j are relaxed to probability variables p j , and include entropy terms for the distributions defined by p j : w T * = arg ⁢ ⁢ min w ∈ ℝ d , { p j ∈ [ 0 ⁢ ⁢ 1 ] } j = 1 u ⁢ λ 2 ⁢  w  2 + 1 2 ⁢ l ⁢ ∑ i = 1 l ⁢ max ⁡ [ 0 , 1 - y i ⁡ ( w T , x i ) ] 2 + λ ′ 2 ⁢ u ⁢ ∑ j = 1 u ⁢ ( p j ⁢ max ⁡ [ 0 , 1 - ( w T ⁢ x j ′ ) ] 2 + ( 1 - p j ) ⁢ max ⁡ [ 0 , 1 + ( w T ⁢ x j ′ ) ] 2 ) + T 2 ⁢ u ⁢ ∑ j = 1 u ⁢ ( p j ⁢ ⁢ log ⁢ ⁢ p j + ( 1 - p j ) ⁢ ⁢ log ⁢ ⁢ ( 1 - p j ) ) ( 8 ) wherein the temperature T parameterizes a family of objective functions, wherein the objective function for a fixed T is minimized under the following class balancing constraints: 1 u ⁢ ∑ j = 1 u ⁢ p j = r where r, the fraction of the number of unlabeled examples belonging to the positive class. 18. The method of claim 15 , comprising optimizing p for a fixed w, a Lagrangian is constructed using Lagrangian multiplier v as follows: ℒ = λ ′ 2 ⁢ u ⁢ ∑ j = 1 u ⁢ ( p j ⁢ ⁢ max ⁡ [ 0 , 1 - ( w T ⁢ x j ′ ) ] 2 + ( 1 - p j ) ⁢ max ⁡ [ 0 , 1 + ( w T ⁢ x j ′ ) ] 2 ) + T 2 ⁢ u ⁢ ∑ j = 1 u ⁢ ( p j ⁢ ⁢ log ⁢ ⁢ p j + ( 1 - p j ) ⁢ ⁢ log ⁢ ⁢ ( 1 - p j ) ) - υ ⁡ [ 1 u ⁢ ∑ j = 1 u ⁢ p j - r ] wherein, differentiating the Lagrangian with respect to p j , produces: ∂ ℒ ∂ p j = λ ′ 2 ⁢ u ⁢ ( max ⁡ [ 0 , 1 - ( w T ⁢ x j ′ ) ] 2 - max ⁡ [ 0 , 1 + ( w T ⁢ x j ′ ) ] 2 ) + ⁢ T 2 ⁢ u ⁢ log ⁢ ⁢ p j 1 - p j - v u = 0 defining 
 g j =λ′(max[0,1−( w T x′ j )] 2 −max[0,1+( w T x′ j )] 2 ), wherein the expression for p j is given by: p j = 1 1 + ⅇ g j - 2 ⁢ v T wherein, substituting the expression for p j in the balance constraint, a one-dimensional non-linear equation in v is as follows: 1 u ⁢ ∑ j = 1 u ⁢ ⁢ 1 1 + ⅇ g i - 2 ⁢ v T = r wherein the value of v is computed exactly by using a hybrid combination of Newton-Raphson iterations and the bisection method to find the root of the function B ⁡ ( v ) = 1 u ⁢ ∑ i = l + 1 l + u ⁢ ⁢ 1 1 + ⅇ g i - v T - r .