Multistep production of $\eta$ and hard $\pi^0$ mesons in subthreshold Au-Au collisions
Abstract
We present an unsupervised approach to cluster sequences. This method is inspired by topology learning methods for hidden Markov models, and is built upon the definition of a distance between Markov models. This type of technique may be used to learn Markovian character models from data or to identify allographs or handwriting styles.