AbstractThe class of very simple grammars is known to be polynomial-time identifiable in the limit from positive data. This paper gives an even more general discussion on the efficiency of identification of very simple grammars from positive data, which includes both positive and negative results. In particular, we present an alternative efficient inconsistent learning algorithm for very simple grammars
Intuitively, a learning algorithm is robust if it can succeed despite adverse conditions. We examine...
acceptance rate 41%Kanazawa has studied the learnability of several parameterized families of classe...
This thesis focuses on the Gold model of inductive inference from positive data. There are several ...
AbstractThe class of very simple grammars is known to be polynomial-time identifiable in the limit f...
AbstractThis paper concerns a subclass of simple deterministic grammars, called very simple grammars...
Learning from positive data constitutes an important topic in Grammatical Inference since it is beli...
AbstractIn this paper, we introduce a new normal form for context-free grammars, called reversible c...
When concerned about efficient grammatical inference two issues are relevant: the first one is to de...
. This paper deals with the polynomial-time learnability of a language class in the limit from posit...
AbstractIn this paper we introduce a paradigm for learning in the limit of potentially infinite lang...
AbstractA new algorithm for learning one-variable pattern languages from positive data is proposed a...
In Gold's influential language learning paradigm a learning machine converges in the limit to one co...
A pattern is a finite string of constant and variable symbols. The language generated by a pattern i...
The eld of Grammatical Inference provides a good theoretical framework for investigating a learning ...
Intuitively, a learning algorithm is robust if it can succeed despite adverse conditions. We examine...
Intuitively, a learning algorithm is robust if it can succeed despite adverse conditions. We examine...
acceptance rate 41%Kanazawa has studied the learnability of several parameterized families of classe...
This thesis focuses on the Gold model of inductive inference from positive data. There are several ...
AbstractThe class of very simple grammars is known to be polynomial-time identifiable in the limit f...
AbstractThis paper concerns a subclass of simple deterministic grammars, called very simple grammars...
Learning from positive data constitutes an important topic in Grammatical Inference since it is beli...
AbstractIn this paper, we introduce a new normal form for context-free grammars, called reversible c...
When concerned about efficient grammatical inference two issues are relevant: the first one is to de...
. This paper deals with the polynomial-time learnability of a language class in the limit from posit...
AbstractIn this paper we introduce a paradigm for learning in the limit of potentially infinite lang...
AbstractA new algorithm for learning one-variable pattern languages from positive data is proposed a...
In Gold's influential language learning paradigm a learning machine converges in the limit to one co...
A pattern is a finite string of constant and variable symbols. The language generated by a pattern i...
The eld of Grammatical Inference provides a good theoretical framework for investigating a learning ...
Intuitively, a learning algorithm is robust if it can succeed despite adverse conditions. We examine...
Intuitively, a learning algorithm is robust if it can succeed despite adverse conditions. We examine...
acceptance rate 41%Kanazawa has studied the learnability of several parameterized families of classe...
This thesis focuses on the Gold model of inductive inference from positive data. There are several ...