Modern dimension reduction:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2021
|
| Schriftenreihe: | Cambridge elements: elements in quantitative and computational methods for the social sciences
|
| Schlagworte: | |
| Online-Zugang: | Klappentext |
| Beschreibung: | 86 Seiten Illustrationen, Diagramme |
| ISBN: | 9781108986892 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV047484240 | ||
| 003 | DE-604 | ||
| 005 | 20220413 | ||
| 007 | t | ||
| 008 | 210927s2021 a||| |||| 00||| eng d | ||
| 020 | |a 9781108986892 |c pbk |9 978-1-108-98689-2 | ||
| 035 | |a (OCoLC)1267705461 | ||
| 035 | |a (DE-599)BVBBV047484240 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-355 | ||
| 084 | |a ST 650 |0 (DE-625)143687: |2 rvk | ||
| 084 | |a ST 301 |0 (DE-625)143651: |2 rvk | ||
| 100 | 1 | |a Waggoner, Philip D. |d ca. 20./21. Jh. |0 (DE-588)1228365040 |4 aut | |
| 245 | 1 | 0 | |a Modern dimension reduction |c Philip D. Waggoner |
| 264 | 1 | |a Cambridge |b Cambridge University Press |c 2021 | |
| 300 | |a 86 Seiten |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Cambridge elements: elements in quantitative and computational methods for the social sciences | |
| 650 | 0 | 7 | |a Computational social science |0 (DE-588)1249405939 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Datenverarbeitung |0 (DE-588)4011152-0 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a R |g Programm |0 (DE-588)4705956-4 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | 1 | |a Datenverarbeitung |0 (DE-588)4011152-0 |D s |
| 689 | 0 | 2 | |a R |g Programm |0 (DE-588)4705956-4 |D s |
| 689 | 0 | 3 | |a Computational social science |0 (DE-588)1249405939 |D s |
| 689 | 0 | |5 DE-604 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-1-108-98176-7 |
| 856 | 4 | 2 | |m Digitalisierung UB Regensburg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032885668&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Klappentext |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-032885668 | ||
Datensatz im Suchindex
| _version_ | 1804182804905328640 |
|---|---|
| adam_text | Data are nöt only ubiquitous in society but are increasingly complex in both size and dimensionality. Dimension reduction offers researchers and scholars the ability to make such complex, high-dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques along with hundreds of lines of R code to efficiently represem the original high-dimensional data space in a simplified, lower-dimensional subspace. Launching from the earliest dimension reduction technique principal components analysis and using real social science data, I introduce and walk readers through application of the following techniques: locally linear embedding, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection, self-organizing maps, and deep autoencoders. The result is a well-stocked toolbox of unsupervised algorithms for tackling the complexities of high dimensional data so common in modern society. All code is publicly accessible on Github. About the Series Series Editors R. Michael Alvarez The Elements Series Quantitative and California Institute Computational Methods for the Social of Technology Sciences contains short introductions and hands-on tutorials to innovative Nathaniel Beck methodologies. These are often so new New York that they have no textbook treatment or University no detailed treatment on how the method is used in practice. Among emerging areas of interest for social scientists, the series presents-machine learning methods, the use of new technologies for the
collection of data and new techniques for assessing causality with experimental and quasiexperimental data.
|
| adam_txt |
Data are nöt only ubiquitous in society but are increasingly complex in both size and dimensionality. Dimension reduction offers researchers and scholars the ability to make such complex, high-dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques along with hundreds of lines of R code to efficiently represem the original high-dimensional data space in a simplified, lower-dimensional subspace. Launching from the earliest dimension reduction technique principal components analysis and using real social science data, I introduce and walk readers through application of the following techniques: locally linear embedding, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection, self-organizing maps, and deep autoencoders. The result is a well-stocked toolbox of unsupervised algorithms for tackling the complexities of high dimensional data so common in modern society. All code is publicly accessible on Github. About the Series Series Editors R. Michael Alvarez The Elements Series Quantitative and California Institute Computational Methods for the Social of Technology Sciences contains short introductions and hands-on tutorials to innovative Nathaniel Beck methodologies. These are often so new New York that they have no textbook treatment or University no detailed treatment on how the method is used in practice. Among emerging areas of interest for social scientists, the series presents-machine learning methods, the use of new technologies for the
collection of data and new techniques for assessing causality with experimental and quasiexperimental data. |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Waggoner, Philip D. ca. 20./21. Jh |
| author_GND | (DE-588)1228365040 |
| author_facet | Waggoner, Philip D. ca. 20./21. Jh |
| author_role | aut |
| author_sort | Waggoner, Philip D. ca. 20./21. Jh |
| author_variant | p d w pd pdw |
| building | Verbundindex |
| bvnumber | BV047484240 |
| classification_rvk | ST 650 ST 301 |
| ctrlnum | (OCoLC)1267705461 (DE-599)BVBBV047484240 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01849nam a2200421 c 4500</leader><controlfield tag="001">BV047484240</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20220413 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">210927s2021 a||| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781108986892</subfield><subfield code="c">pbk</subfield><subfield code="9">978-1-108-98689-2</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1267705461</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV047484240</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-355</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 650</subfield><subfield code="0">(DE-625)143687:</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="100" ind1="1" ind2=" "><subfield code="a">Waggoner, Philip D.</subfield><subfield code="d">ca. 20./21. Jh.</subfield><subfield code="0">(DE-588)1228365040</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Modern dimension reduction</subfield><subfield code="c">Philip D. Waggoner</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cambridge</subfield><subfield code="b">Cambridge University Press</subfield><subfield code="c">2021</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">86 Seiten</subfield><subfield code="b">Illustrationen, Diagramme</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">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Cambridge elements: elements in quantitative and computational methods for the social sciences</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Computational social science</subfield><subfield code="0">(DE-588)1249405939</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Datenverarbeitung</subfield><subfield code="0">(DE-588)4011152-0</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="650" ind1="0" ind2="7"><subfield code="a">R</subfield><subfield code="g">Programm</subfield><subfield code="0">(DE-588)4705956-4</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">Datenverarbeitung</subfield><subfield code="0">(DE-588)4011152-0</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">R</subfield><subfield code="g">Programm</subfield><subfield code="0">(DE-588)4705956-4</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="3"><subfield code="a">Computational social science</subfield><subfield code="0">(DE-588)1249405939</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe</subfield><subfield code="z">978-1-108-98176-7</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Digitalisierung UB Regensburg - ADAM Catalogue Enrichment</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032885668&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Klappentext</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-032885668</subfield></datafield></record></collection> |
| id | DE-604.BV047484240 |
| illustrated | Illustrated |
| index_date | 2024-07-03T18:13:36Z |
| indexdate | 2024-07-10T09:13:23Z |
| institution | BVB |
| isbn | 9781108986892 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032885668 |
| oclc_num | 1267705461 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR |
| owner_facet | DE-355 DE-BY-UBR |
| physical | 86 Seiten Illustrationen, Diagramme |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Cambridge University Press |
| record_format | marc |
| series2 | Cambridge elements: elements in quantitative and computational methods for the social sciences |
| spelling | Waggoner, Philip D. ca. 20./21. Jh. (DE-588)1228365040 aut Modern dimension reduction Philip D. Waggoner Cambridge Cambridge University Press 2021 86 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Cambridge elements: elements in quantitative and computational methods for the social sciences Computational social science (DE-588)1249405939 gnd rswk-swf Datenverarbeitung (DE-588)4011152-0 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Datenverarbeitung (DE-588)4011152-0 s R Programm (DE-588)4705956-4 s Computational social science (DE-588)1249405939 s DE-604 Erscheint auch als Online-Ausgabe 978-1-108-98176-7 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032885668&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Waggoner, Philip D. ca. 20./21. Jh Modern dimension reduction Computational social science (DE-588)1249405939 gnd Datenverarbeitung (DE-588)4011152-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd R Programm (DE-588)4705956-4 gnd |
| subject_GND | (DE-588)1249405939 (DE-588)4011152-0 (DE-588)4193754-5 (DE-588)4705956-4 |
| title | Modern dimension reduction |
| title_auth | Modern dimension reduction |
| title_exact_search | Modern dimension reduction |
| title_exact_search_txtP | Modern dimension reduction |
| title_full | Modern dimension reduction Philip D. Waggoner |
| title_fullStr | Modern dimension reduction Philip D. Waggoner |
| title_full_unstemmed | Modern dimension reduction Philip D. Waggoner |
| title_short | Modern dimension reduction |
| title_sort | modern dimension reduction |
| topic | Computational social science (DE-588)1249405939 gnd Datenverarbeitung (DE-588)4011152-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd R Programm (DE-588)4705956-4 gnd |
| topic_facet | Computational social science Datenverarbeitung Maschinelles Lernen R Programm |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032885668&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT waggonerphilipd moderndimensionreduction |