On learning equal matrix languages:
Abstract: "We consider the learning problem for languages, called strongly bounded equal matrix languages, consisting of strings of the form [formula] where each a[supscript i] is a symbol and n[subscript i] is a nonnegative integer. The languages are defined in terms of certain parallel rewrit...
Saved in:
| Main Author: | |
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| Format: | Book |
| Language: | English |
| Published: |
Tokyo, Japan
1989
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| Series: | Shin-Sedai-Konpyūta-Gijutsu-Kaihatsu-Kikō <Tōkyō>: ICOT technical report
452 |
| Subjects: | |
| Summary: | Abstract: "We consider the learning problem for languages, called strongly bounded equal matrix languages, consisting of strings of the form [formula] where each a[supscript i] is a symbol and n[subscript i] is a nonnegative integer. The languages are defined in terms of certain parallel rewriting grammars called equal matrix grammars. Also, the languages closely related to semilinear subsets of the Cartesian product of nonnegative integers We show that (1) the family of strongly bounded equal matrix languages is not learnable from positive examples, while there exists a meaningful subfamily which is learnable from positive examples, (2) given any teacher, called an ideal teacher, the subfamily is learnable in polynomial time of the size of inputs. |
| Physical Description: | 10 S. |
Staff View
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| 520 | 3 | |a Abstract: "We consider the learning problem for languages, called strongly bounded equal matrix languages, consisting of strings of the form [formula] where each a[supscript i] is a symbol and n[subscript i] is a nonnegative integer. The languages are defined in terms of certain parallel rewriting grammars called equal matrix grammars. Also, the languages closely related to semilinear subsets of the Cartesian product of nonnegative integers | |
| 520 | 3 | |a We show that (1) the family of strongly bounded equal matrix languages is not learnable from positive examples, while there exists a meaningful subfamily which is learnable from positive examples, (2) given any teacher, called an ideal teacher, the subfamily is learnable in polynomial time of the size of inputs. | |
| 650 | 4 | |a Machine learning | |
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| id | DE-604.BV010947099 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-09T18:01:29Z |
| institution | BVB |
| language | English |
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| physical | 10 S. |
| publishDate | 1989 |
| publishDateSearch | 1989 |
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| series | Shin-Sedai-Konpyūta-Gijutsu-Kaihatsu-Kikō <Tōkyō>: ICOT technical report |
| series2 | Shin-Sedai-Konpyūta-Gijutsu-Kaihatsu-Kikō <Tōkyō>: ICOT technical report |
| spelling | Takada, Yuji Verfasser aut On learning equal matrix languages by Y. Takada Tokyo, Japan 1989 10 S. txt rdacontent n rdamedia nc rdacarrier Shin-Sedai-Konpyūta-Gijutsu-Kaihatsu-Kikō <Tōkyō>: ICOT technical report 452 Abstract: "We consider the learning problem for languages, called strongly bounded equal matrix languages, consisting of strings of the form [formula] where each a[supscript i] is a symbol and n[subscript i] is a nonnegative integer. The languages are defined in terms of certain parallel rewriting grammars called equal matrix grammars. Also, the languages closely related to semilinear subsets of the Cartesian product of nonnegative integers We show that (1) the family of strongly bounded equal matrix languages is not learnable from positive examples, while there exists a meaningful subfamily which is learnable from positive examples, (2) given any teacher, called an ideal teacher, the subfamily is learnable in polynomial time of the size of inputs. Machine learning Shin-Sedai-Konpyūta-Gijutsu-Kaihatsu-Kikō <Tōkyō>: ICOT technical report 452 (DE-604)BV010923438 452 |
| spellingShingle | Takada, Yuji On learning equal matrix languages Shin-Sedai-Konpyūta-Gijutsu-Kaihatsu-Kikō <Tōkyō>: ICOT technical report Machine learning |
| title | On learning equal matrix languages |
| title_auth | On learning equal matrix languages |
| title_exact_search | On learning equal matrix languages |
| title_full | On learning equal matrix languages by Y. Takada |
| title_fullStr | On learning equal matrix languages by Y. Takada |
| title_full_unstemmed | On learning equal matrix languages by Y. Takada |
| title_short | On learning equal matrix languages |
| title_sort | on learning equal matrix languages |
| topic | Machine learning |
| topic_facet | Machine learning |
| volume_link | (DE-604)BV010923438 |
| work_keys_str_mv | AT takadayuji onlearningequalmatrixlanguages |