Developer Guide

Contents

Mean Squared Error Algorithm

Given
x
= (
x
i
1
, … ,
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

Product and Performance Information

1

Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

Notice revision #20110804