12. An inference engine comprising a processor programmed to perform an inference process comprising: generating an upper bound by double majorization for a sum-of-exponentials function including at least one summation of exponentials of the form ∑ k = 1 K ⁢ ⅇ β k T ⁢ x constrained by an input object representation vector and an output classification observation vector; optimizing the upper bound respective to parameters β k of the sum-of-exponentials function; and classifying one or more input objects by applying the sum-of-exponentials function with the optimized parameters β k to said one or more input objects.
12. An inference engine comprising a processor programmed to perform an inference process comprising: generating an upper bound by double majorization for a sum-of-exponentials function including at least one summation of exponentials of the form ∑ k = 1 K ⁢ ⅇ β k T ⁢ x constrained by an input object representation vector and an output classification observation vector; optimizing the upper bound respective to parameters β k of the sum-of-exponentials function; and classifying one or more input objects by applying the sum-of-exponentials function with the optimized parameters β k to said one or more input objects. 17. The inference engine as set forth in claim 12 , wherein the input object representation vector includes a features vector representation of at least one image and the classifying performs image classification.