Patent ID: 7107207

Claim:
A system for training a machine learning system, comprising: an expected value update component that, for a plurality of outputs and for a plurality of instances in which a single feature function is non-zero, modifies an expected value based, at least in part, upon the single feature function of an input vector and an output value, a sum of lambda variable and a normalization variable; an error calculator that calculates an error based, at least in part, upon the expected value and an observed value, the error calculation further employing, at least in part, the following equation: observed ⁢ ⁢ value ⁡ [ i ] = ∑ j , v ⁢ P λ _ ⁡ ( x _ j , y ) × f i ⁡ ( x _ j , y ) × exp ⁡ ( δ i ⁢ f i ⁡ ( x _ j , y ) ) where observed value [i] is number of times the feature function is observed, {overscore (λ)} is a trainable parameter vector, δ i is the error, P 80 ({overscore (x)} j , y) is a probability model, ƒ i ({overscore (x)} j , y) is the feature function, {overscore (x)} j is the input vector, i is an instance of the feature function, j is a training instance, and, y is the output value; a parameter update component that modifies a trainable parameter based, at least in part, upon the error; and, a variable update component that, for the plurality of outputs and for the plurality of instances in which the feature function is non-zero, sequentially updates at least one of the sum of lambda variable and the normalization variable based, at least in part, upon the error.