Eintrag weiter verarbeiten
Automated Machine Learning Overview
Gespeichert in:
| Zeitschriftentitel: | Research Papers Faculty of Materials Science and Technology Slovak University of Technology |
|---|---|
| Personen und Körperschaften: | , , , , |
| In: | Research Papers Faculty of Materials Science and Technology Slovak University of Technology, 27, 2019, 45, S. 107-112 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Walter de Gruyter GmbH
|
| Schlagwörter: |
| author_facet |
Budjač, Roman Nikmon, Marcel Schreiber, Peter Zahradníková, Barbora Janáčová, Dagmar Budjač, Roman Nikmon, Marcel Schreiber, Peter Zahradníková, Barbora Janáčová, Dagmar |
|---|---|
| author |
Budjač, Roman Nikmon, Marcel Schreiber, Peter Zahradníková, Barbora Janáčová, Dagmar |
| spellingShingle |
Budjač, Roman Nikmon, Marcel Schreiber, Peter Zahradníková, Barbora Janáčová, Dagmar Research Papers Faculty of Materials Science and Technology Slovak University of Technology Automated Machine Learning Overview General Earth and Planetary Sciences General Environmental Science |
| author_sort |
budjač, roman |
| spelling |
Budjač, Roman Nikmon, Marcel Schreiber, Peter Zahradníková, Barbora Janáčová, Dagmar 1338-0532 Walter de Gruyter GmbH General Earth and Planetary Sciences General Environmental Science http://dx.doi.org/10.2478/rput-2019-0033 <jats:title>Abstract</jats:title> <jats:p>This paper aims at deeper exploration of the new field named auto-machine learning, as it shows promising results in specific machine learning tasks e.g. image classification. The following article is about to summarize the most successful approaches now available in the A.I. community. The automated machine learning method is very briefly described here, but the concept of automated task solving seems to be very promising, since it can significantly reduce expertise level of a person developing the machine learning model. We used Auto-Keras to find the best architecture on several datasets, and demonstrated several automated machine learning features, as well as discussed the issue deeper.</jats:p> Automated Machine Learning Overview Research Papers Faculty of Materials Science and Technology Slovak University of Technology |
| doi_str_mv |
10.2478/rput-2019-0033 |
| facet_avail |
Online Free |
| format |
ElectronicArticle |
| fullrecord |
blob:ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMjQ3OC9ycHV0LTIwMTktMDAzMw |
| id |
ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMjQ3OC9ycHV0LTIwMTktMDAzMw |
| institution |
DE-D275 DE-Bn3 DE-Brt1 DE-Zwi2 DE-D161 DE-Gla1 DE-Zi4 DE-15 DE-Pl11 DE-Rs1 DE-105 DE-14 DE-Ch1 DE-L229 |
| imprint |
Walter de Gruyter GmbH, 2019 |
| imprint_str_mv |
Walter de Gruyter GmbH, 2019 |
| issn |
1338-0532 |
| issn_str_mv |
1338-0532 |
| language |
English |
| mega_collection |
Walter de Gruyter GmbH (CrossRef) |
| match_str |
budjac2019automatedmachinelearningoverview |
| publishDateSort |
2019 |
| publisher |
Walter de Gruyter GmbH |
| recordtype |
ai |
| record_format |
ai |
| series |
Research Papers Faculty of Materials Science and Technology Slovak University of Technology |
| source_id |
49 |
| title |
Automated Machine Learning Overview |
| title_unstemmed |
Automated Machine Learning Overview |
| title_full |
Automated Machine Learning Overview |
| title_fullStr |
Automated Machine Learning Overview |
| title_full_unstemmed |
Automated Machine Learning Overview |
| title_short |
Automated Machine Learning Overview |
| title_sort |
automated machine learning overview |
| topic |
General Earth and Planetary Sciences General Environmental Science |
| url |
http://dx.doi.org/10.2478/rput-2019-0033 |
| publishDate |
2019 |
| physical |
107-112 |
| description |
<jats:title>Abstract</jats:title>
<jats:p>This paper aims at deeper exploration of the new field named auto-machine learning, as it shows promising results in specific machine learning tasks e.g. image classification. The following article is about to summarize the most successful approaches now available in the A.I. community. The automated machine learning method is very briefly described here, but the concept of automated task solving seems to be very promising, since it can significantly reduce expertise level of a person developing the machine learning model. We used Auto-Keras to find the best architecture on several datasets, and demonstrated several automated machine learning features, as well as discussed the issue deeper.</jats:p> |
| container_issue |
45 |
| container_start_page |
107 |
| container_title |
Research Papers Faculty of Materials Science and Technology Slovak University of Technology |
| container_volume |
27 |
| format_de105 |
Article, E-Article |
| format_de14 |
Article, E-Article |
| format_de15 |
Article, E-Article |
| format_de520 |
Article, E-Article |
| format_de540 |
Article, E-Article |
| format_dech1 |
Article, E-Article |
| format_ded117 |
Article, E-Article |
| format_degla1 |
E-Article |
| format_del152 |
Buch |
| format_del189 |
Article, E-Article |
| format_dezi4 |
Article |
| format_dezwi2 |
Article, E-Article |
| format_finc |
Article, E-Article |
| format_nrw |
Article, E-Article |
| _version_ |
1792344026099220480 |
| geogr_code |
not assigned |
| last_indexed |
2024-03-01T17:00:38.8Z |
| geogr_code_person |
not assigned |
| openURL |
url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fvufind.svn.sourceforge.net%3Agenerator&rft.title=Automated+Machine+Learning+Overview&rft.date=2019-09-01&genre=article&issn=1338-0532&volume=27&issue=45&spage=107&epage=112&pages=107-112&jtitle=Research+Papers+Faculty+of+Materials+Science+and+Technology+Slovak+University+of+Technology&atitle=Automated+Machine+Learning+Overview&aulast=Jan%C3%A1%C4%8Dov%C3%A1&aufirst=Dagmar&rft_id=info%3Adoi%2F10.2478%2Frput-2019-0033&rft.language%5B0%5D=eng |
| SOLR | |
| _version_ | 1792344026099220480 |
| author | Budjač, Roman, Nikmon, Marcel, Schreiber, Peter, Zahradníková, Barbora, Janáčová, Dagmar |
| author_facet | Budjač, Roman, Nikmon, Marcel, Schreiber, Peter, Zahradníková, Barbora, Janáčová, Dagmar, Budjač, Roman, Nikmon, Marcel, Schreiber, Peter, Zahradníková, Barbora, Janáčová, Dagmar |
| author_sort | budjač, roman |
| container_issue | 45 |
| container_start_page | 107 |
| container_title | Research Papers Faculty of Materials Science and Technology Slovak University of Technology |
| container_volume | 27 |
| description | <jats:title>Abstract</jats:title> <jats:p>This paper aims at deeper exploration of the new field named auto-machine learning, as it shows promising results in specific machine learning tasks e.g. image classification. The following article is about to summarize the most successful approaches now available in the A.I. community. The automated machine learning method is very briefly described here, but the concept of automated task solving seems to be very promising, since it can significantly reduce expertise level of a person developing the machine learning model. We used Auto-Keras to find the best architecture on several datasets, and demonstrated several automated machine learning features, as well as discussed the issue deeper.</jats:p> |
| doi_str_mv | 10.2478/rput-2019-0033 |
| facet_avail | Online, Free |
| format | ElectronicArticle |
| format_de105 | Article, E-Article |
| format_de14 | Article, E-Article |
| format_de15 | Article, E-Article |
| format_de520 | Article, E-Article |
| format_de540 | Article, E-Article |
| format_dech1 | Article, E-Article |
| format_ded117 | Article, E-Article |
| format_degla1 | E-Article |
| format_del152 | Buch |
| format_del189 | Article, E-Article |
| format_dezi4 | Article |
| format_dezwi2 | Article, E-Article |
| format_finc | Article, E-Article |
| format_nrw | Article, E-Article |
| geogr_code | not assigned |
| geogr_code_person | not assigned |
| id | ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMjQ3OC9ycHV0LTIwMTktMDAzMw |
| imprint | Walter de Gruyter GmbH, 2019 |
| imprint_str_mv | Walter de Gruyter GmbH, 2019 |
| institution | DE-D275, DE-Bn3, DE-Brt1, DE-Zwi2, DE-D161, DE-Gla1, DE-Zi4, DE-15, DE-Pl11, DE-Rs1, DE-105, DE-14, DE-Ch1, DE-L229 |
| issn | 1338-0532 |
| issn_str_mv | 1338-0532 |
| language | English |
| last_indexed | 2024-03-01T17:00:38.8Z |
| match_str | budjac2019automatedmachinelearningoverview |
| mega_collection | Walter de Gruyter GmbH (CrossRef) |
| physical | 107-112 |
| publishDate | 2019 |
| publishDateSort | 2019 |
| publisher | Walter de Gruyter GmbH |
| record_format | ai |
| recordtype | ai |
| series | Research Papers Faculty of Materials Science and Technology Slovak University of Technology |
| source_id | 49 |
| spelling | Budjač, Roman Nikmon, Marcel Schreiber, Peter Zahradníková, Barbora Janáčová, Dagmar 1338-0532 Walter de Gruyter GmbH General Earth and Planetary Sciences General Environmental Science http://dx.doi.org/10.2478/rput-2019-0033 <jats:title>Abstract</jats:title> <jats:p>This paper aims at deeper exploration of the new field named auto-machine learning, as it shows promising results in specific machine learning tasks e.g. image classification. The following article is about to summarize the most successful approaches now available in the A.I. community. The automated machine learning method is very briefly described here, but the concept of automated task solving seems to be very promising, since it can significantly reduce expertise level of a person developing the machine learning model. We used Auto-Keras to find the best architecture on several datasets, and demonstrated several automated machine learning features, as well as discussed the issue deeper.</jats:p> Automated Machine Learning Overview Research Papers Faculty of Materials Science and Technology Slovak University of Technology |
| spellingShingle | Budjač, Roman, Nikmon, Marcel, Schreiber, Peter, Zahradníková, Barbora, Janáčová, Dagmar, Research Papers Faculty of Materials Science and Technology Slovak University of Technology, Automated Machine Learning Overview, General Earth and Planetary Sciences, General Environmental Science |
| title | Automated Machine Learning Overview |
| title_full | Automated Machine Learning Overview |
| title_fullStr | Automated Machine Learning Overview |
| title_full_unstemmed | Automated Machine Learning Overview |
| title_short | Automated Machine Learning Overview |
| title_sort | automated machine learning overview |
| title_unstemmed | Automated Machine Learning Overview |
| topic | General Earth and Planetary Sciences, General Environmental Science |
| url | http://dx.doi.org/10.2478/rput-2019-0033 |