Validity, Reliability, and Significance: Empirical Methods for NLP and Data Science
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer Nature Switzerland
2024
Cham Springer |
| Ausgabe: | 2nd ed. 2024 |
| Schriftenreihe: | Synthesis Lectures on Human Language Technologies
|
| Schlagworte: | |
| Online-Zugang: | DE-573 DE-92 DE-703 URL des Erstveröffentlichers |
| Beschreibung: | 1 Online-Ressource (XVII, 168 p. 70 illus., 61 illus. in color) |
| ISBN: | 9783031570650 |
| ISSN: | 1947-4059 |
| DOI: | 10.1007/978-3-031-57065-0 |
Internformat
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| 650 | 4 | |a Design of Experiments | |
| 650 | 4 | |a Natural Language Processing (NLP) | |
| 650 | 4 | |a Data Science | |
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| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Computer science / Mathematics | |
| 650 | 4 | |a Mathematical statistics | |
| 650 | 4 | |a Experimental design | |
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Datensatz im Suchindex
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| adam_text | |
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| author | Riezler, Stefan Hagmann, Michael |
| author_facet | Riezler, Stefan Hagmann, Michael |
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| author_sort | Riezler, Stefan |
| author_variant | s r sr m h mh |
| building | Verbundindex |
| bvnumber | BV049780874 |
| collection | ZDB-2-SXSC |
| ctrlnum | (ZDB-2-SXSC)9783031570650 (OCoLC)1446260502 (DE-599)BVBBV049780874 |
| 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 | 10.1007/978-3-031-57065-0 |
| edition | 2nd ed. 2024 |
| format | Electronic eBook |
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| id | DE-604.BV049780874 |
| illustrated | Not Illustrated |
| indexdate | 2024-08-01T00:26:16Z |
| institution | BVB |
| isbn | 9783031570650 |
| issn | 1947-4059 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035121855 |
| oclc_num | 1446260502 |
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| owner_facet | DE-92 DE-573 DE-703 |
| physical | 1 Online-Ressource (XVII, 168 p. 70 illus., 61 illus. in color) |
| psigel | ZDB-2-SXSC ZDB-2-SXSC_2024 |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Springer Nature Switzerland Springer |
| record_format | marc |
| series2 | Synthesis Lectures on Human Language Technologies |
| spelling | Riezler, Stefan Verfasser aut Validity, Reliability, and Significance Empirical Methods for NLP and Data Science by Stefan Riezler, Michael Hagmann 2nd ed. 2024 Cham Springer Nature Switzerland 2024 Cham Springer 1 Online-Ressource (XVII, 168 p. 70 illus., 61 illus. in color) txt rdacontent c rdamedia cr rdacarrier Synthesis Lectures on Human Language Technologies 1947-4059 Artificial Intelligence Machine Learning Probability and Statistics in Computer Science Design of Experiments Natural Language Processing (NLP) Data Science Artificial intelligence Machine learning Computer science / Mathematics Mathematical statistics Experimental design Natural language processing (Computer science) Artificial intelligence / Data processing Hagmann, Michael aut Erscheint auch als Druck-Ausgabe 978-3-031-57064-3 Erscheint auch als Druck-Ausgabe 978-3-031-57066-7 Erscheint auch als Druck-Ausgabe 978-3-031-57067-4 https://doi.org/10.1007/978-3-031-57065-0 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Riezler, Stefan Hagmann, Michael Validity, Reliability, and Significance Empirical Methods for NLP and Data Science Artificial Intelligence Machine Learning Probability and Statistics in Computer Science Design of Experiments Natural Language Processing (NLP) Data Science Artificial intelligence Machine learning Computer science / Mathematics Mathematical statistics Experimental design Natural language processing (Computer science) Artificial intelligence / Data processing |
| title | Validity, Reliability, and Significance Empirical Methods for NLP and Data Science |
| title_auth | Validity, Reliability, and Significance Empirical Methods for NLP and Data Science |
| title_exact_search | Validity, Reliability, and Significance Empirical Methods for NLP and Data Science |
| title_full | Validity, Reliability, and Significance Empirical Methods for NLP and Data Science by Stefan Riezler, Michael Hagmann |
| title_fullStr | Validity, Reliability, and Significance Empirical Methods for NLP and Data Science by Stefan Riezler, Michael Hagmann |
| title_full_unstemmed | Validity, Reliability, and Significance Empirical Methods for NLP and Data Science by Stefan Riezler, Michael Hagmann |
| title_short | Validity, Reliability, and Significance |
| title_sort | validity reliability and significance empirical methods for nlp and data science |
| title_sub | Empirical Methods for NLP and Data Science |
| topic | Artificial Intelligence Machine Learning Probability and Statistics in Computer Science Design of Experiments Natural Language Processing (NLP) Data Science Artificial intelligence Machine learning Computer science / Mathematics Mathematical statistics Experimental design Natural language processing (Computer science) Artificial intelligence / Data processing |
| topic_facet | Artificial Intelligence Machine Learning Probability and Statistics in Computer Science Design of Experiments Natural Language Processing (NLP) Data Science Artificial intelligence Machine learning Computer science / Mathematics Mathematical statistics Experimental design Natural language processing (Computer science) Artificial intelligence / Data processing |
| url | https://doi.org/10.1007/978-3-031-57065-0 |
| work_keys_str_mv | AT riezlerstefan validityreliabilityandsignificanceempiricalmethodsfornlpanddatascience AT hagmannmichael validityreliabilityandsignificanceempiricalmethodsfornlpanddatascience |