Mean Squared Error Algorithm

Given x = (x i1 , … , x ip ) ∈ R p , a set of feature vectors i ∈ {1, ..., n} , and a set of respective responses y i , the mean squared error (MSE) objective function F(θ; x, y) is a function that has the format:

, where j=1,…,p.

In Intel DAAL implementation of the MSE, the h(θ, y i ) function is a represented as:

For a given set of the indices I = {i 1, i 2, ... , i m }, 1 ≤ i r < n, l ∈ {1, ..., m}, |I| = m, the value and the gradient of the sum of functions in the argument x respectively have the format:

where

For more complete information about compiler optimizations, see our Optimization Notice.
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