Learning theory: an approximation theory viewpoint
The goal of learning theory is to approximate a function from sample values. To attain this goal learning theory draws on a variety of diverse subjects, specifically statistics, approximation theory, and algorithmics. Ideas from all these areas blended to form a subject whose many successful applica...
Gespeichert in:
Hauptverfasser: | , |
---|---|
Format: | Elektronisch E-Book |
Sprache: | English |
Veröffentlicht: |
Cambridge
Cambridge University Press
2007
|
Schriftenreihe: | Cambridge monographs on applied and computational mathematics
24 |
Schlagworte: | |
Online-Zugang: | BSB01 FHN01 UPA01 URL des Erstveröffentlichers |
Zusammenfassung: | The goal of learning theory is to approximate a function from sample values. To attain this goal learning theory draws on a variety of diverse subjects, specifically statistics, approximation theory, and algorithmics. Ideas from all these areas blended to form a subject whose many successful applications have triggered a rapid growth during the last two decades. This is the first book to give a general overview of the theoretical foundations of the subject emphasizing the approximation theory, while still giving a balanced overview. It is based on courses taught by the authors, and is reasonably self-contained so will appeal to a broad spectrum of researchers in learning theory and adjacent fields. It will also serve as an introduction for graduate students and others entering the field, who wish to see how the problems raised in learning theory relate to other disciplines |
Beschreibung: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
Beschreibung: | 1 online resource (xii, 224 pages) |
ISBN: | 9780511618796 |
DOI: | 10.1017/CBO9780511618796 |
Internformat
MARC
LEADER | 00000nmm a2200000zcb4500 | ||
---|---|---|---|
001 | BV043940769 | ||
003 | DE-604 | ||
005 | 20221122 | ||
007 | cr|uuu---uuuuu | ||
008 | 161206s2007 |||| o||u| ||||||eng d | ||
020 | |a 9780511618796 |c Online |9 978-0-511-61879-6 | ||
024 | 7 | |a 10.1017/CBO9780511618796 |2 doi | |
035 | |a (ZDB-20-CBO)CR9780511618796 | ||
035 | |a (OCoLC)850353812 | ||
035 | |a (DE-599)BVBBV043940769 | ||
040 | |a DE-604 |b ger |e rda | ||
041 | 0 | |a eng | |
049 | |a DE-12 |a DE-92 |a DE-739 | ||
082 | 0 | |a 006.3/1 |2 22 | |
084 | |a ST 130 |0 (DE-625)143588: |2 rvk | ||
084 | |a ST 301 |0 (DE-625)143651: |2 rvk | ||
084 | |a ST 302 |0 (DE-625)143652: |2 rvk | ||
084 | |a SK 470 |0 (DE-625)143241: |2 rvk | ||
100 | 1 | |a Cucker, Felipe |d 1958- |e Verfasser |0 (DE-588)133832783 |4 aut | |
245 | 1 | 0 | |a Learning theory |b an approximation theory viewpoint |c Felipe Cucker, Ding-Xuan Zhou |
264 | 1 | |a Cambridge |b Cambridge University Press |c 2007 | |
300 | |a 1 online resource (xii, 224 pages) | ||
336 | |b txt |2 rdacontent | ||
337 | |b c |2 rdamedia | ||
338 | |b cr |2 rdacarrier | ||
490 | 0 | |a Cambridge monographs on applied and computational mathematics |v 24 | |
500 | |a Title from publisher's bibliographic system (viewed on 05 Oct 2015) | ||
520 | |a The goal of learning theory is to approximate a function from sample values. To attain this goal learning theory draws on a variety of diverse subjects, specifically statistics, approximation theory, and algorithmics. Ideas from all these areas blended to form a subject whose many successful applications have triggered a rapid growth during the last two decades. This is the first book to give a general overview of the theoretical foundations of the subject emphasizing the approximation theory, while still giving a balanced overview. It is based on courses taught by the authors, and is reasonably self-contained so will appeal to a broad spectrum of researchers in learning theory and adjacent fields. It will also serve as an introduction for graduate students and others entering the field, who wish to see how the problems raised in learning theory relate to other disciplines | ||
650 | 4 | |a Computational learning theory | |
650 | 4 | |a Approximation theory | |
650 | 0 | 7 | |a Approximationstheorie |0 (DE-588)4120913-8 |2 gnd |9 rswk-swf |
650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
689 | 0 | 0 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
689 | 0 | 1 | |a Approximationstheorie |0 (DE-588)4120913-8 |D s |
689 | 0 | |8 1\p |5 DE-604 | |
700 | 1 | |a Zhou, Ding-Xuan |e Verfasser |0 (DE-588)113762224 |4 aut | |
776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 978-0-521-86559-3 |
856 | 4 | 0 | |u https://doi.org/10.1017/CBO9780511618796 |x Verlag |z URL des Erstveröffentlichers |3 Volltext |
912 | |a ZDB-20-CBO | ||
999 | |a oai:aleph.bib-bvb.de:BVB01-029349739 | ||
883 | 1 | |8 1\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
966 | e | |u https://doi.org/10.1017/CBO9780511618796 |l BSB01 |p ZDB-20-CBO |q BSB_PDA_CBO |x Verlag |3 Volltext | |
966 | e | |u https://doi.org/10.1017/CBO9780511618796 |l FHN01 |p ZDB-20-CBO |q FHN_PDA_CBO |x Verlag |3 Volltext | |
966 | e | |u https://doi.org/10.1017/CBO9780511618796 |l UPA01 |p ZDB-20-CBO |q UPA_PDA_CBO_Kauf2020 |x Verlag |3 Volltext |
Datensatz im Suchindex
_version_ | 1804176881683005440 |
---|---|
any_adam_object | |
author | Cucker, Felipe 1958- Zhou, Ding-Xuan |
author_GND | (DE-588)133832783 (DE-588)113762224 |
author_facet | Cucker, Felipe 1958- Zhou, Ding-Xuan |
author_role | aut aut |
author_sort | Cucker, Felipe 1958- |
author_variant | f c fc d x z dxz |
building | Verbundindex |
bvnumber | BV043940769 |
classification_rvk | ST 130 ST 301 ST 302 SK 470 |
collection | ZDB-20-CBO |
ctrlnum | (ZDB-20-CBO)CR9780511618796 (OCoLC)850353812 (DE-599)BVBBV043940769 |
dewey-full | 006.3/1 |
dewey-hundreds | 000 - Computer science, information, general works |
dewey-ones | 006 - Special computer methods |
dewey-raw | 006.3/1 |
dewey-search | 006.3/1 |
dewey-sort | 16.3 11 |
dewey-tens | 000 - Computer science, information, general works |
discipline | Informatik Mathematik |
doi_str_mv | 10.1017/CBO9780511618796 |
format | Electronic eBook |
fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03245nmm a2200553zcb4500</leader><controlfield tag="001">BV043940769</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20221122 </controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">161206s2007 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780511618796</subfield><subfield code="c">Online</subfield><subfield code="9">978-0-511-61879-6</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1017/CBO9780511618796</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-20-CBO)CR9780511618796</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)850353812</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV043940769</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-12</subfield><subfield code="a">DE-92</subfield><subfield code="a">DE-739</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">006.3/1</subfield><subfield code="2">22</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 130</subfield><subfield code="0">(DE-625)143588:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 301</subfield><subfield code="0">(DE-625)143651:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 302</subfield><subfield code="0">(DE-625)143652:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">SK 470</subfield><subfield code="0">(DE-625)143241:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Cucker, Felipe</subfield><subfield code="d">1958-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)133832783</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Learning theory</subfield><subfield code="b">an approximation theory viewpoint</subfield><subfield code="c">Felipe Cucker, Ding-Xuan Zhou</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cambridge</subfield><subfield code="b">Cambridge University Press</subfield><subfield code="c">2007</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (xii, 224 pages)</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">Cambridge monographs on applied and computational mathematics</subfield><subfield code="v">24</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Title from publisher's bibliographic system (viewed on 05 Oct 2015)</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The goal of learning theory is to approximate a function from sample values. To attain this goal learning theory draws on a variety of diverse subjects, specifically statistics, approximation theory, and algorithmics. Ideas from all these areas blended to form a subject whose many successful applications have triggered a rapid growth during the last two decades. This is the first book to give a general overview of the theoretical foundations of the subject emphasizing the approximation theory, while still giving a balanced overview. It is based on courses taught by the authors, and is reasonably self-contained so will appeal to a broad spectrum of researchers in learning theory and adjacent fields. It will also serve as an introduction for graduate students and others entering the field, who wish to see how the problems raised in learning theory relate to other disciplines</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computational learning theory</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Approximation theory</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Approximationstheorie</subfield><subfield code="0">(DE-588)4120913-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Approximationstheorie</subfield><subfield code="0">(DE-588)4120913-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="8">1\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Ding-Xuan</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)113762224</subfield><subfield code="4">aut</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">978-0-521-86559-3</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1017/CBO9780511618796</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-20-CBO</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-029349739</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">1\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/CBO9780511618796</subfield><subfield code="l">BSB01</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">BSB_PDA_CBO</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/CBO9780511618796</subfield><subfield code="l">FHN01</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">FHN_PDA_CBO</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/CBO9780511618796</subfield><subfield code="l">UPA01</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">UPA_PDA_CBO_Kauf2020</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
id | DE-604.BV043940769 |
illustrated | Not Illustrated |
indexdate | 2024-07-10T07:39:14Z |
institution | BVB |
isbn | 9780511618796 |
language | English |
oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029349739 |
oclc_num | 850353812 |
open_access_boolean | |
owner | DE-12 DE-92 DE-739 |
owner_facet | DE-12 DE-92 DE-739 |
physical | 1 online resource (xii, 224 pages) |
psigel | ZDB-20-CBO ZDB-20-CBO BSB_PDA_CBO ZDB-20-CBO FHN_PDA_CBO ZDB-20-CBO UPA_PDA_CBO_Kauf2020 |
publishDate | 2007 |
publishDateSearch | 2007 |
publishDateSort | 2007 |
publisher | Cambridge University Press |
record_format | marc |
series2 | Cambridge monographs on applied and computational mathematics |
spelling | Cucker, Felipe 1958- Verfasser (DE-588)133832783 aut Learning theory an approximation theory viewpoint Felipe Cucker, Ding-Xuan Zhou Cambridge Cambridge University Press 2007 1 online resource (xii, 224 pages) txt rdacontent c rdamedia cr rdacarrier Cambridge monographs on applied and computational mathematics 24 Title from publisher's bibliographic system (viewed on 05 Oct 2015) The goal of learning theory is to approximate a function from sample values. To attain this goal learning theory draws on a variety of diverse subjects, specifically statistics, approximation theory, and algorithmics. Ideas from all these areas blended to form a subject whose many successful applications have triggered a rapid growth during the last two decades. This is the first book to give a general overview of the theoretical foundations of the subject emphasizing the approximation theory, while still giving a balanced overview. It is based on courses taught by the authors, and is reasonably self-contained so will appeal to a broad spectrum of researchers in learning theory and adjacent fields. It will also serve as an introduction for graduate students and others entering the field, who wish to see how the problems raised in learning theory relate to other disciplines Computational learning theory Approximation theory Approximationstheorie (DE-588)4120913-8 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Approximationstheorie (DE-588)4120913-8 s 1\p DE-604 Zhou, Ding-Xuan Verfasser (DE-588)113762224 aut Erscheint auch als Druck-Ausgabe 978-0-521-86559-3 https://doi.org/10.1017/CBO9780511618796 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
spellingShingle | Cucker, Felipe 1958- Zhou, Ding-Xuan Learning theory an approximation theory viewpoint Computational learning theory Approximation theory Approximationstheorie (DE-588)4120913-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
subject_GND | (DE-588)4120913-8 (DE-588)4193754-5 |
title | Learning theory an approximation theory viewpoint |
title_auth | Learning theory an approximation theory viewpoint |
title_exact_search | Learning theory an approximation theory viewpoint |
title_full | Learning theory an approximation theory viewpoint Felipe Cucker, Ding-Xuan Zhou |
title_fullStr | Learning theory an approximation theory viewpoint Felipe Cucker, Ding-Xuan Zhou |
title_full_unstemmed | Learning theory an approximation theory viewpoint Felipe Cucker, Ding-Xuan Zhou |
title_short | Learning theory |
title_sort | learning theory an approximation theory viewpoint |
title_sub | an approximation theory viewpoint |
topic | Computational learning theory Approximation theory Approximationstheorie (DE-588)4120913-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
topic_facet | Computational learning theory Approximation theory Approximationstheorie Maschinelles Lernen |
url | https://doi.org/10.1017/CBO9780511618796 |
work_keys_str_mv | AT cuckerfelipe learningtheoryanapproximationtheoryviewpoint AT zhoudingxuan learningtheoryanapproximationtheoryviewpoint |