Here is the result of my efforts for obtaining the Master of Science degree
at the Université Laval in
the Master's degree computer science program. My memoir
is entitled: "Nouveaux algorithmes d'apprentissage pour classificateurs de type
SCM". Its abstract is given in the frame below.
In the supervised machine learning field, one of the available tools for binary
classification is the Set Covering Machine (SCM). Quickly built and generally
having high performance, it's however not proven that they always give optimal
results. There is still, to date, a margin for improvement.
This study presents two new ways of building SCM. Theses algorithms are described,
explained and their performance is analyzed. The first way is to minimize an
approximated bound on the risk with a branch-and-bound. The second is using
The new classifiers had the same test-set performance than the original SCM.
We discovered that the latter are either already optimal according to the
branch-and-bound criterion or having the same performance as the optimal SCM.
During my work, I've implemented the search in a text with the
Boyer-Moore algorithm in C++.
For the downloads table in the section on
Half-Life modifications, I've implemented a merge sort in