We propose and analyze a distribution learning algorithm for variable memory length Markov processes. These processes can be described by a subclass of probabilistic nite automata...
Mining for outliers in sequential databases is crucial to forward appropriate analysis of data. Therefore, many approaches for the discovery of such anomalies have been proposed. ...
We consider the problem of PAC-learning distributions over strings, represented by probabilistic deterministic finite automata (PDFAs). PDFAs are a probabilistic model for the gen...
Bounded Model Checking (BMC) is a successful refutation method to detect errors in not only circuits and other binary systems but also in systems with more complex domains like ti...
A new algorithm for learning one-variable pattern languages is proposed and analyzed with respect to its average-case behavior. We consider the total learning time that takes into...