Computational learning theory and natural learning systems:
This is the fourth and final volume of papers from a series of workshops called "Computational Learning Theory and `Natural' Learning Systems." The purpose of the workshops was to explore the emerging intersection of theoretical learning research and natural learning systems. The work...
Saved in:
| Other Authors: | , , |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Cambridge, Mass.
MIT Press
|
| Series: | A Bradford Book
|
| Links: | https://doi.org/10.7551/mitpress/2016.001.0001?locatt=mode:legacy |
| Summary: | This is the fourth and final volume of papers from a series of workshops called "Computational Learning Theory and `Natural' Learning Systems." The purpose of the workshops was to explore the emerging intersection of theoretical learning research and natural learning systems. The workshops drew researchers from three historically distinct styles of learning research: computational learning theory, neural networks, and machine learning (a subfield of AI).Volume I of the series introduces the general focus of the workshops. Volume II looks at specific areas of interaction between theory and experiment. Volumes III and IV focus on key areas of learning systems that have developed recently. Volume III looks at the problem of "Selecting Good Models." The present volume, Volume IV, looks at ways of "Making Learning Systems Practical." The editors divide the twenty-one contributions into four sections. The first three cover critical problem areas: 1) scaling up from small problems to realistic ones with large input dimensions, 2) increasing efficiency and robustness of learning methods, and 3) developing strategies to obtain good generalization from limited or small data samples. The fourth section discusses examples of real-world learning systems.ContributorsKlaus Abraham-Fuchs, Yasuhiro Akiba, Hussein Almuallim, Arunava Banerjee, Sanjay Bhansali, Alvis Brazma, Gustavo Deco, David Garvin, Zoubin Ghahramani, Mostefa Golea, Russell Greiner, Mehdi T. Harandi, John G. Harris, Haym Hirsh, Michael I. Jordan, Shigeo Kaneda, Marjorie Klenin, Pat Langley, Yong Liu, Patrick M. Murphy, Ralph Neuneier, E. M. Oblow, Dragan Obradovic, Michael J. Pazzani, Barak A. Pearlmutter, Nageswara S. V. Rao, Peter Rayner, Stephanie Sage, Martin F. Schlang, Bernd Schurmann, Dale Schuurmans, Leon Shklar, V. Sundareswaran, Geoffrey Towell, Johann Uebler, Lucia M. Vaina, Takefumi Yamazaki, Anthony M. Zador |
| Physical Description: | 1 Online-Ressource (xxiii, 407 Seiten) Illustrationen |
| ISBN: | 0262291134 9780262291132 |
Staff View
MARC
| LEADER | 00000cam a2200000Ka 4500 | ||
|---|---|---|---|
| 001 | ZDB-260-MPOB-2016 | ||
| 003 | MaCbMITP | ||
| 005 | 20190503073411.0 | ||
| 006 | m o d | ||
| 007 | cr cnu---unuuu | ||
| 008 | 130212s1997 maua o 100 0 eng d | ||
| 020 | |a 0262291134 | ||
| 020 | |a 9780262291132 | ||
| 245 | 0 | 0 | |a Computational learning theory and natural learning systems |
| 264 | 1 | |a Cambridge, Mass. |b MIT Press | |
| 300 | |a 1 Online-Ressource (xxiii, 407 Seiten) |b Illustrationen | ||
| 336 | |b txt | ||
| 337 | |b c | ||
| 338 | |b cr | ||
| 490 | 0 | |a A Bradford Book | |
| 520 | |a This is the fourth and final volume of papers from a series of workshops called "Computational Learning Theory and `Natural' Learning Systems." The purpose of the workshops was to explore the emerging intersection of theoretical learning research and natural learning systems. The workshops drew researchers from three historically distinct styles of learning research: computational learning theory, neural networks, and machine learning (a subfield of AI).Volume I of the series introduces the general focus of the workshops. Volume II looks at specific areas of interaction between theory and experiment. Volumes III and IV focus on key areas of learning systems that have developed recently. Volume III looks at the problem of "Selecting Good Models." The present volume, Volume IV, looks at ways of "Making Learning Systems Practical." The editors divide the twenty-one contributions into four sections. The first three cover critical problem areas: 1) scaling up from small problems to realistic ones with large input dimensions, 2) increasing efficiency and robustness of learning methods, and 3) developing strategies to obtain good generalization from limited or small data samples. The fourth section discusses examples of real-world learning systems.ContributorsKlaus Abraham-Fuchs, Yasuhiro Akiba, Hussein Almuallim, Arunava Banerjee, Sanjay Bhansali, Alvis Brazma, Gustavo Deco, David Garvin, Zoubin Ghahramani, Mostefa Golea, Russell Greiner, Mehdi T. Harandi, John G. Harris, Haym Hirsh, Michael I. Jordan, Shigeo Kaneda, Marjorie Klenin, Pat Langley, Yong Liu, Patrick M. Murphy, Ralph Neuneier, E. M. Oblow, Dragan Obradovic, Michael J. Pazzani, Barak A. Pearlmutter, Nageswara S. V. Rao, Peter Rayner, Stephanie Sage, Martin F. Schlang, Bernd Schurmann, Dale Schuurmans, Leon Shklar, V. Sundareswaran, Geoffrey Towell, Johann Uebler, Lucia M. Vaina, Takefumi Yamazaki, Anthony M. Zador | ||
| 700 | 1 | |a Greiner, Russell | |
| 700 | 1 | |a Hanson, Stephen José | |
| 700 | 1 | |a Petsche, Thomas | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 0262571188 |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 9780262571180 |
| 966 | 4 | 0 | |l DE-91 |p ZDB-260-MPOB |q TUM_PDA_MPOB |3 MIT Press |u https://doi.org/10.7551/mitpress/2016.001.0001?locatt=mode:legacy |3 Volltext |
| 912 | |a ZDB-260-MPOB | ||
| 912 | |a ZDB-260-MPOB | ||
| 049 | |a DE-91 | ||
Record in the Search Index
| DE-BY-TUM_katkey | ZDB-260-MPOB-2016 |
|---|---|
| _version_ | 1821493840223141890 |
| adam_text | |
| any_adam_object | |
| author2 | Greiner, Russell Hanson, Stephen José Petsche, Thomas |
| author2_role | |
| author2_variant | r g rg s j h sj sjh t p tp |
| author_facet | Greiner, Russell Hanson, Stephen José Petsche, Thomas |
| author_sort | Greiner, Russell |
| building | Verbundindex |
| bvnumber | localTUM |
| collection | ZDB-260-MPOB |
| format | eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02815cam a2200301Ka 4500</leader><controlfield tag="001">ZDB-260-MPOB-2016</controlfield><controlfield tag="003">MaCbMITP</controlfield><controlfield tag="005">20190503073411.0</controlfield><controlfield tag="006">m o d </controlfield><controlfield tag="007">cr cnu---unuuu</controlfield><controlfield tag="008">130212s1997 maua o 100 0 eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">0262291134</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780262291132</subfield></datafield><datafield tag="245" ind1="0" ind2="0"><subfield code="a">Computational learning theory and natural learning systems</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cambridge, Mass.</subfield><subfield code="b">MIT Press</subfield><subfield code="c"></subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (xxiii, 407 Seiten)</subfield><subfield code="b">Illustrationen</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">A Bradford Book</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This is the fourth and final volume of papers from a series of workshops called "Computational Learning Theory and `Natural' Learning Systems." The purpose of the workshops was to explore the emerging intersection of theoretical learning research and natural learning systems. The workshops drew researchers from three historically distinct styles of learning research: computational learning theory, neural networks, and machine learning (a subfield of AI).Volume I of the series introduces the general focus of the workshops. Volume II looks at specific areas of interaction between theory and experiment. Volumes III and IV focus on key areas of learning systems that have developed recently. Volume III looks at the problem of "Selecting Good Models." The present volume, Volume IV, looks at ways of "Making Learning Systems Practical." The editors divide the twenty-one contributions into four sections. The first three cover critical problem areas: 1) scaling up from small problems to realistic ones with large input dimensions, 2) increasing efficiency and robustness of learning methods, and 3) developing strategies to obtain good generalization from limited or small data samples. The fourth section discusses examples of real-world learning systems.ContributorsKlaus Abraham-Fuchs, Yasuhiro Akiba, Hussein Almuallim, Arunava Banerjee, Sanjay Bhansali, Alvis Brazma, Gustavo Deco, David Garvin, Zoubin Ghahramani, Mostefa Golea, Russell Greiner, Mehdi T. Harandi, John G. Harris, Haym Hirsh, Michael I. Jordan, Shigeo Kaneda, Marjorie Klenin, Pat Langley, Yong Liu, Patrick M. Murphy, Ralph Neuneier, E. M. Oblow, Dragan Obradovic, Michael J. Pazzani, Barak A. Pearlmutter, Nageswara S. V. Rao, Peter Rayner, Stephanie Sage, Martin F. Schlang, Bernd Schurmann, Dale Schuurmans, Leon Shklar, V. Sundareswaran, Geoffrey Towell, Johann Uebler, Lucia M. Vaina, Takefumi Yamazaki, Anthony M. Zador</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Greiner, Russell</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hanson, Stephen José</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Petsche, Thomas</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">0262571188</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">9780262571180</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-91</subfield><subfield code="p">ZDB-260-MPOB</subfield><subfield code="q">TUM_PDA_MPOB</subfield><subfield code="3">MIT Press</subfield><subfield code="u">https://doi.org/10.7551/mitpress/2016.001.0001?locatt=mode:legacy</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-260-MPOB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-260-MPOB</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91</subfield></datafield></record></collection> |
| id | ZDB-260-MPOB-2016 |
| illustrated | Illustrated |
| indexdate | 2025-01-17T11:04:52Z |
| institution | BVB |
| isbn | 0262291134 9780262291132 |
| language | English |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (xxiii, 407 Seiten) Illustrationen |
| psigel | ZDB-260-MPOB TUM_PDA_MPOB ZDB-260-MPOB |
| publishDateSearch | 1997 |
| publishDateSort | 1997 |
| publisher | MIT Press |
| record_format | marc |
| series2 | A Bradford Book |
| spelling | Computational learning theory and natural learning systems Cambridge, Mass. MIT Press 1 Online-Ressource (xxiii, 407 Seiten) Illustrationen txt c cr A Bradford Book This is the fourth and final volume of papers from a series of workshops called "Computational Learning Theory and `Natural' Learning Systems." The purpose of the workshops was to explore the emerging intersection of theoretical learning research and natural learning systems. The workshops drew researchers from three historically distinct styles of learning research: computational learning theory, neural networks, and machine learning (a subfield of AI).Volume I of the series introduces the general focus of the workshops. Volume II looks at specific areas of interaction between theory and experiment. Volumes III and IV focus on key areas of learning systems that have developed recently. Volume III looks at the problem of "Selecting Good Models." The present volume, Volume IV, looks at ways of "Making Learning Systems Practical." The editors divide the twenty-one contributions into four sections. The first three cover critical problem areas: 1) scaling up from small problems to realistic ones with large input dimensions, 2) increasing efficiency and robustness of learning methods, and 3) developing strategies to obtain good generalization from limited or small data samples. The fourth section discusses examples of real-world learning systems.ContributorsKlaus Abraham-Fuchs, Yasuhiro Akiba, Hussein Almuallim, Arunava Banerjee, Sanjay Bhansali, Alvis Brazma, Gustavo Deco, David Garvin, Zoubin Ghahramani, Mostefa Golea, Russell Greiner, Mehdi T. Harandi, John G. Harris, Haym Hirsh, Michael I. Jordan, Shigeo Kaneda, Marjorie Klenin, Pat Langley, Yong Liu, Patrick M. Murphy, Ralph Neuneier, E. M. Oblow, Dragan Obradovic, Michael J. Pazzani, Barak A. Pearlmutter, Nageswara S. V. Rao, Peter Rayner, Stephanie Sage, Martin F. Schlang, Bernd Schurmann, Dale Schuurmans, Leon Shklar, V. Sundareswaran, Geoffrey Towell, Johann Uebler, Lucia M. Vaina, Takefumi Yamazaki, Anthony M. Zador Greiner, Russell Hanson, Stephen José Petsche, Thomas Erscheint auch als Druck-Ausgabe 0262571188 Erscheint auch als Druck-Ausgabe 9780262571180 |
| spellingShingle | Computational learning theory and natural learning systems |
| title | Computational learning theory and natural learning systems |
| title_auth | Computational learning theory and natural learning systems |
| title_exact_search | Computational learning theory and natural learning systems |
| title_full | Computational learning theory and natural learning systems |
| title_fullStr | Computational learning theory and natural learning systems |
| title_full_unstemmed | Computational learning theory and natural learning systems |
| title_short | Computational learning theory and natural learning systems |
| title_sort | computational learning theory and natural learning systems |
| work_keys_str_mv | AT greinerrussell computationallearningtheoryandnaturallearningsystems AT hansonstephenjose computationallearningtheoryandnaturallearningsystems AT petschethomas computationallearningtheoryandnaturallearningsystems |