Developer Guide

Contents

Details

Given
n
feature vectors
x
1
=(
x
11
,…,
x
1
p
),...,
x
n
=(
x
n
1
,…,
x
np
) of size
p
, a vector of class labels y=(
y
1
,…,
y
n
), where
y
i
K
= {-1, 1} describes the class to which the feature vector
x
i
belongs, and a weak learner algorithm, the problem is to build an AdaBoost classifier.

Training Stage

The following scheme shows the major steps of the algorithm:
  1. Initialize weights
    D
    1
    (
    i
    ) = 1/
    n
    for
    i
    = 1,...,
    n
  2. For
    t
    = 1,...,
    T
    :
    1. Train the weak learner
      h
      t
      (
      t
      )
      {-1, 1} using weights
      D
      t
    2. Choose a confidence value
      α
      t
    3. Update
      where
      Z
      t
      is a normalization factor
  3. Output the final hypothesis:

Prediction Stage

Given the AdaBoost classifier and
r
feature vectors
x
1
,…,
x
r
, the problem is to calculate the final class

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