Connectionist Approaches to Language Learning:
arise automatically as a result of the recursive structure of the task and the continuous nature of the SRN's state space. Elman also introduces a new graphical technique for study ing network behavior based on principal components analysis. He shows that sentences with multiple levels of embe...
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
1991
|
| Schriftenreihe: | The Springer International Series in Engineering and Computer Science
154 |
| Schlagworte: | |
| Online-Zugang: | BTU01 URL des Erstveröffentlichers |
| Zusammenfassung: | arise automatically as a result of the recursive structure of the task and the continuous nature of the SRN's state space. Elman also introduces a new graphical technique for study ing network behavior based on principal components analysis. He shows that sentences with multiple levels of embedding produce state space trajectories with an intriguing self similar structure. The development and shape of a recurrent network's state space is the subject of Pollack's paper, the most provocative in this collection. Pollack looks more closely at a connectionist network as a continuous dynamical system. He describes a new type of machine learning phenomenon: induction by phase transition. He then shows that under certain conditions, the state space created by these machines can have a fractal or chaotic structure, with a potentially infinite number of states. This is graphically illustrated using a higher-order recurrent network trained to recognize various regular languages over binary strings. Finally, Pollack suggests that it might be possible to exploit the fractal dynamics of these systems to achieve a generative capacity beyond that of finite-state machines |
| Beschreibung: | 1 Online-Ressource (IV, 149 p) |
| ISBN: | 9781461540083 |
| DOI: | 10.1007/978-1-4615-4008-3 |
Internformat
MARC
| LEADER | 00000nmm a2200000zcb4500 | ||
|---|---|---|---|
| 001 | BV045184804 | ||
| 003 | DE-604 | ||
| 005 | 00000000000000.0 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 180912s1991 |||| o||u| ||||||eng d | ||
| 020 | |a 9781461540083 |9 978-1-4615-4008-3 | ||
| 024 | 7 | |a 10.1007/978-1-4615-4008-3 |2 doi | |
| 035 | |a (ZDB-2-ENG)978-1-4615-4008-3 | ||
| 035 | |a (OCoLC)1053826326 | ||
| 035 | |a (DE-599)BVBBV045184804 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-634 | ||
| 082 | 0 | |a 006.3 |2 23 | |
| 245 | 1 | 0 | |a Connectionist Approaches to Language Learning |c edited by David Touretzky |
| 264 | 1 | |a Boston, MA |b Springer US |c 1991 | |
| 300 | |a 1 Online-Ressource (IV, 149 p) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a The Springer International Series in Engineering and Computer Science |v 154 | |
| 520 | |a arise automatically as a result of the recursive structure of the task and the continuous nature of the SRN's state space. Elman also introduces a new graphical technique for study ing network behavior based on principal components analysis. He shows that sentences with multiple levels of embedding produce state space trajectories with an intriguing self similar structure. The development and shape of a recurrent network's state space is the subject of Pollack's paper, the most provocative in this collection. Pollack looks more closely at a connectionist network as a continuous dynamical system. He describes a new type of machine learning phenomenon: induction by phase transition. He then shows that under certain conditions, the state space created by these machines can have a fractal or chaotic structure, with a potentially infinite number of states. This is graphically illustrated using a higher-order recurrent network trained to recognize various regular languages over binary strings. Finally, Pollack suggests that it might be possible to exploit the fractal dynamics of these systems to achieve a generative capacity beyond that of finite-state machines | ||
| 650 | 4 | |a Computer Science | |
| 650 | 4 | |a Artificial Intelligence (incl. Robotics) | |
| 650 | 4 | |a Statistical Physics, Dynamical Systems and Complexity | |
| 650 | 4 | |a Computer Science, general | |
| 650 | 4 | |a Computer science | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Statistical physics | |
| 650 | 4 | |a Dynamical systems | |
| 700 | 1 | |a Touretzky, David |4 edt | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 9781461367925 |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-1-4615-4008-3 |x Verlag |z URL des Erstveröffentlichers |3 Volltext |
| 912 | |a ZDB-2-ENG | ||
| 940 | 1 | |q ZDB-2-ENG_Archiv | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-030573981 | ||
| 966 | e | |u https://doi.org/10.1007/978-1-4615-4008-3 |l BTU01 |p ZDB-2-ENG |q ZDB-2-ENG_Archiv |x Verlag |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1804178873976356864 |
|---|---|
| any_adam_object | |
| author2 | Touretzky, David |
| author2_role | edt |
| author2_variant | d t dt |
| author_facet | Touretzky, David |
| building | Verbundindex |
| bvnumber | BV045184804 |
| collection | ZDB-2-ENG |
| ctrlnum | (ZDB-2-ENG)978-1-4615-4008-3 (OCoLC)1053826326 (DE-599)BVBBV045184804 |
| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| doi_str_mv | 10.1007/978-1-4615-4008-3 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02832nmm a2200469zcb4500</leader><controlfield tag="001">BV045184804</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">180912s1991 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781461540083</subfield><subfield code="9">978-1-4615-4008-3</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/978-1-4615-4008-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-2-ENG)978-1-4615-4008-3</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1053826326</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV045184804</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">aacr</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-634</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">006.3</subfield><subfield code="2">23</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Connectionist Approaches to Language Learning</subfield><subfield code="c">edited by David Touretzky</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Boston, MA</subfield><subfield code="b">Springer US</subfield><subfield code="c">1991</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (IV, 149 p)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">The Springer International Series in Engineering and Computer Science</subfield><subfield code="v">154</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">arise automatically as a result of the recursive structure of the task and the continuous nature of the SRN's state space. Elman also introduces a new graphical technique for study ing network behavior based on principal components analysis. He shows that sentences with multiple levels of embedding produce state space trajectories with an intriguing self similar structure. The development and shape of a recurrent network's state space is the subject of Pollack's paper, the most provocative in this collection. Pollack looks more closely at a connectionist network as a continuous dynamical system. He describes a new type of machine learning phenomenon: induction by phase transition. He then shows that under certain conditions, the state space created by these machines can have a fractal or chaotic structure, with a potentially infinite number of states. This is graphically illustrated using a higher-order recurrent network trained to recognize various regular languages over binary strings. Finally, Pollack suggests that it might be possible to exploit the fractal dynamics of these systems to achieve a generative capacity beyond that of finite-state machines</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer Science</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial Intelligence (incl. Robotics)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Statistical Physics, Dynamical Systems and Complexity</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer Science, general</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer science</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Statistical physics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dynamical systems</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Touretzky, David</subfield><subfield code="4">edt</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">9781461367925</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1007/978-1-4615-4008-3</subfield><subfield code="x">Verlag</subfield><subfield code="z">URL des Erstveröffentlichers</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-2-ENG</subfield></datafield><datafield tag="940" ind1="1" ind2=" "><subfield code="q">ZDB-2-ENG_Archiv</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-030573981</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/978-1-4615-4008-3</subfield><subfield code="l">BTU01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="q">ZDB-2-ENG_Archiv</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV045184804 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:10:54Z |
| institution | BVB |
| isbn | 9781461540083 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030573981 |
| oclc_num | 1053826326 |
| open_access_boolean | |
| owner | DE-634 |
| owner_facet | DE-634 |
| physical | 1 Online-Ressource (IV, 149 p) |
| psigel | ZDB-2-ENG ZDB-2-ENG_Archiv ZDB-2-ENG ZDB-2-ENG_Archiv |
| publishDate | 1991 |
| publishDateSearch | 1991 |
| publishDateSort | 1991 |
| publisher | Springer US |
| record_format | marc |
| series2 | The Springer International Series in Engineering and Computer Science |
| spelling | Connectionist Approaches to Language Learning edited by David Touretzky Boston, MA Springer US 1991 1 Online-Ressource (IV, 149 p) txt rdacontent c rdamedia cr rdacarrier The Springer International Series in Engineering and Computer Science 154 arise automatically as a result of the recursive structure of the task and the continuous nature of the SRN's state space. Elman also introduces a new graphical technique for study ing network behavior based on principal components analysis. He shows that sentences with multiple levels of embedding produce state space trajectories with an intriguing self similar structure. The development and shape of a recurrent network's state space is the subject of Pollack's paper, the most provocative in this collection. Pollack looks more closely at a connectionist network as a continuous dynamical system. He describes a new type of machine learning phenomenon: induction by phase transition. He then shows that under certain conditions, the state space created by these machines can have a fractal or chaotic structure, with a potentially infinite number of states. This is graphically illustrated using a higher-order recurrent network trained to recognize various regular languages over binary strings. Finally, Pollack suggests that it might be possible to exploit the fractal dynamics of these systems to achieve a generative capacity beyond that of finite-state machines Computer Science Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity Computer Science, general Computer science Artificial intelligence Statistical physics Dynamical systems Touretzky, David edt Erscheint auch als Druck-Ausgabe 9781461367925 https://doi.org/10.1007/978-1-4615-4008-3 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Connectionist Approaches to Language Learning Computer Science Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity Computer Science, general Computer science Artificial intelligence Statistical physics Dynamical systems |
| title | Connectionist Approaches to Language Learning |
| title_auth | Connectionist Approaches to Language Learning |
| title_exact_search | Connectionist Approaches to Language Learning |
| title_full | Connectionist Approaches to Language Learning edited by David Touretzky |
| title_fullStr | Connectionist Approaches to Language Learning edited by David Touretzky |
| title_full_unstemmed | Connectionist Approaches to Language Learning edited by David Touretzky |
| title_short | Connectionist Approaches to Language Learning |
| title_sort | connectionist approaches to language learning |
| topic | Computer Science Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity Computer Science, general Computer science Artificial intelligence Statistical physics Dynamical systems |
| topic_facet | Computer Science Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity Computer Science, general Computer science Artificial intelligence Statistical physics Dynamical systems |
| url | https://doi.org/10.1007/978-1-4615-4008-3 |
| work_keys_str_mv | AT touretzkydavid connectionistapproachestolanguagelearning |