A new approach in machine learning
This work addresses efficiency challenges in machine learning for practitioners, though it appears incremental as it builds on existing boolean circuit concepts.
The authors tackled the problem of improving efficiency in machine learning by proposing a framework based on boolean circuits, which uses bits and boolean gates instead of real numbers, resulting in classifiers that are extremely efficient and compare favorably in accuracy to conventional techniques.
In this technical report we presented a novel approach to machine learning. Once the new framework is presented, we will provide a simple and yet very powerful learning algorithm which will be benchmark on various dataset. The framework we proposed is based on booleen circuits; more specifically the classifier produced by our algorithm have that form. Using bits and boolean gates instead of real numbers and multiplication enable the the learning algorithm and classifier to use very efficient boolean vector operations. This enable both the learning algorithm and classifier to be extremely efficient. The accuracy of the classifier we obtain with our framework compares very favorably those produced by conventional techniques, both in terms of efficiency and accuracy.