  The use of machine learning for building a classifier in signal processing
for motion sensing presents unique challenges. This paper proposes a novel
method that effectively addresses the combination of skewed data sets and
optimization requirements. By utilizing a customized loss function and a
product of probability models, our approach achieves a fully automated and
efficient machine learning process. Additionally, our resulting probability
models offer reduced complexity, making them ideal for embedded applications.
Our method offers a promising solution for motion sensing applications that
require accurate and efficient classification.
