Learning PDFA with asynchronous transitions

TL;DR: The methods for inference of probability distributions generated by probabilistic automata and related models for sequence generation are reviewed, focusing on methods that can be proved to learn in the inference in the limit and PAC formal models.

TL;DR: The proposed detection method is demonstrated to be highly effective in matching traffic from various malware types to pre-learned fingerprints, capable of detecting a variety of threats on network data with low false alarm rates.

TL;DR: In this article, the authors propose a PAC framework for learning both the topology and the parameters in partially observable Markov models, which can approximate a Hidden Markov Model (HMM) up to some desired degree of accuracy.

TL;DR: This thesis gives the first application of this method for learning conditional distributions over pairs of aligned sequences and proves that the method can learn the whole class of probabilistic automata, thus extending the class of models previously known to be learnable with this approach.

TL;DR: In this article, an Ontology-based user model for web personalization is presented. But the model is not suitable for the task of tutoring, as it is unsuitable for a large number of students.

TL;DR: It is demonstrated that the class of PDFAs is PAC-learnable using a variant of a standard state-merging algorithm and the Kullback-Leibler divergence as error function.

TL;DR: In this paper, a distribution learning algorithm for a subclass ofacyclic probalistic finite automata (APFA) is proposed and analyzed, which is characterized by a certain distinguishability property of the a...

TL;DR: The architecture of AUGURES is described, a system that learns to predict Web user's intentions for visiting the site as well its resource usage and uses these predictions to automatically shape QoS for the most profitable sessions, predict short-term resource needs, and dynamically provision servers according to the expected revenue and the cost to serve it.