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Random DFA's can be Approximately Learned from Sparse Uniform Examples
, 1998
"... Approximate inference of finite state machines from sparse labeled examples has been proved NPhard when an adversary chooses the target machine and the training set [Ang78, KV89, PW89]. We have, however, empirically found that DFA's are approximately learnable from sparse data when the target ..."
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Cited by 82 (3 self)
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Approximate inference of finite state machines from sparse labeled examples has been proved NPhard when an adversary chooses the target machine and the training set [Ang78, KV89, PW89]. We have, however, empirically found that DFA's are approximately learnable from sparse data when the target
Exact Dfa identification using Sat solvers
 In Proceedings of ICGI’10, volume 6339 of Lncs
, 2010
"... Abstract. We present an exact algorithm for identification of deterministic finite automata (DFA) which is based on satisfiability (SAT) solvers. Despite the size of the low level SAT representation, our approach is competitive with alternative techniques. Our contributions are fourfold: First, we p ..."
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Cited by 7 (3 self)
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. Fourth, we show how to use the flexibility of our translation in order to apply it to very hard problems. Experiments on a wellknown suite of random DFA identification problems show that SAT solvers can efficiently tackle all instances. Moreover, our algorithm outperforms stateoftheart techniques
Learning DFA from Simple Examples
, 1997
"... Efficient learning of DFA is a challenging research problem in grammatical inference. It is known that both exact and approximate (in the PAC sense) identifiability of DFA is hard. Pitt, in his seminal paper posed the following open research problem: "Are DFAPACidentifiable if examples are d ..."
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Cited by 22 (5 self)
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prove that if the examples are sampled at random according to the universal distribution by a teacher that is knowledgeable about the target concept, the entire class of DFA is efficiently PAC learnable under the universal distribution. Thus, we show that DFA are efficiently learnable under the PACS
A Randomized Parallel Algorithm for DfaMinimization
"... The problem of finding the coarsest partition of a set S with respect to another partition of S and one or more functions on S has several applications, one of which is the state minimization of finite state automata. The problem has a well known O(n log n) sequential algorithm. In this paper, we p ..."
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present efficient parallel randomised algorithms for the problem. Keywords: Parallelalgorithms, Partitioning, DFA minimization 1 Introduction The single function coarsest partitioning problem can be stated as follows: Problem : Given a set S of n elements, a 1 ; a 2 ; :::; a n , a partition ß = (ß 1
Learning DFA: Evolution versus Evidence Driven State Merging
"... Learning Deterministic Finite Automata (DFA) is a hard task that has been much studied within machine learning and evolutionary computation research. This paper presents a new method for evolving DFAs, where only the transition matrix is evolved, and the state labels are chosen to optimize the fit b ..."
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between final states and training set labels. This new procedure reduces the size and in particular, the complexity, of the search space. We present results on the Tomita languages, and also on a set of random DFA induction problems of varying target size and training set density. The Tomita set results
The Query Complexity of Learning DFA \Lambda
"... It is known that the class of deterministic finite automata is polynomial time learnable by using membership and equivalence queries. We investigate the query complexity of learning deterministic finite automata, i.e., the number of membership and equivalence queries made during the process of learn ..."
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of learning. We extend a known lower bound on membership queries to the case of randomized learning algorithms, and prove lower bounds on the number of alternations between membership and equivalence queries. We also show that a tradeoff exists, allowing us to reduce the number of equivalence queries
A GAuGE Approach to Learning DFA from Noisy Samples
"... Abstract. This paper describes the adaptation of the GAuGE system to classify binary sequences generated by random DFA. Experiments were conducted, which, although not highly successful, illustrate the potential of applying GAuGE like systems to this problem domain. 1 The Problem The problem was sta ..."
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Abstract. This paper describes the adaptation of the GAuGE system to classify binary sequences generated by random DFA. Experiments were conducted, which, although not highly successful, illustrate the potential of applying GAuGE like systems to this problem domain. 1 The Problem The problem
Investigations of Cardiac Rhythm Fluctuation Using the DFA Method
"... Considering the highly nonlinear and non stationary features of the ECG signal proven by latest researches, the most appropriate methods of analysis are based also on the nonlinear dynamics; we used a modified root mean square analysis of a random walk, named detrended fluctuations analysis (DFA), w ..."
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Considering the highly nonlinear and non stationary features of the ECG signal proven by latest researches, the most appropriate methods of analysis are based also on the nonlinear dynamics; we used a modified root mean square analysis of a random walk, named detrended fluctuations analysis (DFA
Ensuring File Authenticity in Private DFA Evaluation on Encrypted Files in the Cloud
"... Abstract. Cloud storage, and more specifically the encryption of file contents to protect them in the cloud, can interfere with access to these files by partially trusted thirdparty service providers and customers. To support such access for patternmatching applications (e.g., malware scanning), w ..."
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), we present a protocol that enables a client authorized by the data owner to evaluate a deterministic finite automaton (DFA) on a file stored at a server (the cloud), even though the file is encrypted by the data owner for protection from the server. Our protocol contributes over previous work
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