Zobrazeno 1 - 10
of 108
pro vyhledávání: '"Fisman, Dana"'
Families of DFAs (FDFAs) are a computational model recognizing $\omega$-regular languages. They were introduced in the quest of finding a Myhill-Nerode theorem for $\omega$-regular languages, and obtaining learning algorithms. FDFAs have been shown t
Externí odkaz:
http://arxiv.org/abs/2310.16022
The problem of learning a computational model from examples has been receiving growing attention. For the particularly challenging problem of learning models of distributed systems, existing results are restricted to models with a fixed number of int
Externí odkaz:
http://arxiv.org/abs/2306.14284
Autor:
Angluin, Dana, Fisman, Dana
Publikováno v:
Logical Methods in Computer Science, Volume 20, Issue 4 (November 8, 2024) lmcs:12283
A characteristic sample for a language $L$ and a learning algorithm $\textbf{L}$ is a finite sample of words $T_L$ labeled by their membership in $L$ such that for any sample $T \supseteq T_L$ consistent with $L$, on input $T$ the learning algorithm
Externí odkaz:
http://arxiv.org/abs/2209.09336
Publikováno v:
Logical Methods in Computer Science, Volume 19, Issue 1 (February 8, 2023) lmcs:9223
The problem of identifying a probabilistic context free grammar has two aspects: the first is determining the grammar's topology (the rules of the grammar) and the second is estimating probabilistic weights for each rule. Given the hardness results f
Externí odkaz:
http://arxiv.org/abs/2203.09441
We prove that the normalized edit distance proposed in [Marzal and Vidal 1993] is a metric when the cost of all the edit operations are the same. This closes a long standing gap in the literature where several authors noted that this distance does no
Externí odkaz:
http://arxiv.org/abs/2201.06115
Publikováno v:
Logical Methods in Computer Science, Volume 19, Issue 2 (April 20, 2023) lmcs:8899
We study the learnability of symbolic finite state automata (SFA), a model shown useful in many applications in software verification. The state-of-the-art literature on this topic follows the query learning paradigm, and so far all obtained results
Externí odkaz:
http://arxiv.org/abs/2112.14252
The problem of identifying a probabilistic context free grammar has two aspects: the first is determining the grammar's topology (the rules of the grammar) and the second is estimating probabilistic weights for each rule. Given the hardness results f
Externí odkaz:
http://arxiv.org/abs/2011.07472
We define the problem of learning a transducer ${S}$ from a target language $U$ containing possibly conflicting transducers, using membership queries and conjecture queries. The requirement is that the language of ${S}$ be a subset of $U$. We argue t
Externí odkaz:
http://arxiv.org/abs/2011.07630
We revisit the complexity of procedures on SFAs (such as intersection, emptiness, etc.) and analyze them according to the measures we find suitable for symbolic automata: the number of states, the maximal number of transitions exiting a state, and th
Externí odkaz:
http://arxiv.org/abs/2011.05389
We address the problem of learning human-interpretable descriptions of a complex system from a finite set of positive and negative examples of its behavior. In contrast to most of the recent work in this area, which focuses on descriptions expressed
Externí odkaz:
http://arxiv.org/abs/2002.03668