##
__A little about LocBoost__

LocBoost is a boosting meta-algorithm for boosting
weak classifiers using a locality-based approach, proposed by Ronny
Meir,Ran El-Yaniv
and Shai
Ben-David of the Technion.

LocBoost classifies an instance by combining locally
weighted mixtures of probabilistic classifiers; in other words it combines
a number of easy to compute classifiers, each of which is an ?expert? in
a designated subset of all the instances, into one strong classifier.

The algorithm is incremental, in the sense that each
iteration takes the previous results and improves them.

The basic flow of each iteration is:

**1.
Try to locate the area that contains the most classification errors, and
guess the weight that should be given to a classifier that is an expert
in this area.**

**2.
Using the parameters of this area and the weight of the classifiers as
an initial guess, maximize the log-likelihood of the combined classifier
that will be created after adding the new weak classifier to the ones already
used.**

**3.
Add the new weak classifier with the optimized parameters calculated to
the classifiers being used.**

A
classification for an instance is created by combining the individual classifications
of each weak classifier that was part of the process, multiplied by their
respective (normalized) weights.

The
classification is confidence-based - the decision for an instance is given
as a probability of that instance belonging to a class.

Much more about LocBoost and the flow of each phase
in the iteration ? in the paper.