Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract    deals with two problems: (1) what makes languages learnable in the limit by natural strategies of varying hardness, and (2) what makes classes of languages the hardest ones to learn. To quantify hardness of learning,  use  intrinsic complexity  based on reductions beten learning problems. Two types of reductions are considered: ak reductions mapping texts (representations of languages) to texts and strong reductions mapping languages to languages. For both types of reductions, characterizations of complete (hardest) classes in terms of their algorithmic and topological potentials have been obtained. To characterize the strong complete degree,  discovered a new and natural complete class capable of “coding” any learning problem using density of the set of rational numbers.  have also discovered and characterized rich hierarchies of degrees of complexity based on “core” natural learning problems. The classes in these hierarchies contain “multidimensional” languages, where the information learned from one dimension aids in learning other dimensions. In one formalization of this idea, the grammars learned from the dimensions 1, 2, …,  k  specify the “subspace” for the dimension  k +1, while the learning strategy for every dimension is predefined. In our other formalization, a “pattern” learned from the dimension  k  specifies the learning strategy for the dimension  k +1. A number of open problems are discussed.
['Sanjay Jain', 'Efim B. Kinber', 'Rolf Wiehagen']
I am writing a research paper on a idea Language Learning From Texts: Degrees of Instrinsic Complexity and Their Characterizations
[{"from": "human", "value": "I am writing a research paper on a idea Language Learning From Texts: Degrees of Instrinsic Complexity and Their Characterizations"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract    deals with two problems: (1) what makes languages learnable in the limit by natural strategies of varying hardness, and (2) what makes classes of languages the hardest ones to learn. To quantify hardness of learning,  use  intrinsic complexity  based on reductions beten learning problems. Two types of reductions are considered: ak reductions mapping texts (representations of languages) to texts and strong reductions mapping languages to languages. For both types of reductions, characterizations of complete (hardest) classes in terms of their algorithmic and topological potentials have been obtained. To characterize the strong complete degree,  discovered a new and natural complete class capable of \u201ccoding\u201d any learning problem using density of the set of rational numbers.  have also discovered and characterized rich hierarchies of degrees of complexity based on \u201ccore\u201d natural learning problems. The classes in these hierarchies contain \u201cmultidimensional\u201d languages, where the information learned from one dimension aids in learning other dimensions. In one formalization of this idea, the grammars learned from the dimensions 1,\u00a02,\u00a0\u2026,\u00a0 k  specify the \u201csubspace\u201d for the dimension  k +1, while the learning strategy for every dimension is predefined. In our other formalization, a \u201cpattern\u201d learned from the dimension  k  specifies the learning strategy for the dimension  k +1. A number of open problems are discussed."}]