Learning PDFA with Asynchronous Transitions
نویسندگان
چکیده
In this paper we extend the PAC learning algorithm due to Clark and Thollard for learning distributions generated by PDFA to automata whose transitions may take varying time lengths, governed by exponential distributions.
منابع مشابه
Probabilistic Deterministic Infinite Automata
We propose a novel Bayesian nonparametric approach to learning with probabilistic deterministic finite automata (PDFA). We define a PDFA with an infinite number of states (probabilistic deterministic infinite automata, or PDIA) and show how to average over its connectivity structure and state-specific emission distributions. Given a finite training sequence, posterior inference in the PDIA can ...
متن کاملProbabilistic Deterministic Infinite Automata
We propose a novel Bayesian nonparametric approach to learning with probabilistic deterministic finite automata (PDFA). We define and develop a sampler for a PDFA with an infinite number of states which we call the probabilistic deterministic infinite automata (PDIA). Posterior predictive inference in this model, given a finite training sequence, can be interpreted as averaging over multiple PD...
متن کاملFlexible State-Merging for Learning (P)DFAs in Python
We present a Python package for learning (non-)probabilistic deterministic finite state automata and provide heuristics in the red-blue framework. As our package is built along the API of the popular scikit-learn package, it is easy to use and new learning methods are easy to add. It provides PDFA learning as an additional tool for sequence prediction or classification to data scientists, witho...
متن کاملA Lower Bound for Learning Distributions Generated by Probabilistic Automata
Known algorithms for learning PDFA can only be shown to run in time polynomial in the so-called distinguishability μ of the target machine, besides the number of states and the usual accuracy and confidence parameters. We show that the dependence on μ is necessary for every algorithm whose structure resembles existing ones. As a technical tool, a new variant of Statistical Queries termed L∞-que...
متن کاملLearning probabilistic automata: A study in state distinguishability
Known algorithms for learning PDFA can only be shown to run in time polynomial in the so-called distinguishability μ of the target machine, besides the number of states and the usual accuracy and confidence parameters. We show that the dependence on μ is necessary in the worst case for every algorithm whose structure resembles existing ones. As a technical tool, a new variant of Statistical Que...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2010