AUTOMATED MACHINE LEARNING OVERVIEW

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F19%3A63524121" target="_blank" >RIV/70883521:28140/19:63524121 - isvavai.cz</a>

Výsledek na webu

<a href="https://content.sciendo.com/view/journals/rput/27/45/article-p107.xml" target="_blank" >https://content.sciendo.com/view/journals/rput/27/45/article-p107.xml</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2478/rput-2019-0033" target="_blank" >10.2478/rput-2019-0033</a>

Jazyk výsledku

angličtina

Název v původním jazyce

AUTOMATED MACHINE LEARNING OVERVIEW

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

AUTOMATED MACHINE LEARNING OVERVIEW

Popis výsledku anglicky

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

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Research Papers Faculty of Materials Science and Technology Slovak University of Technology

ISSN

1338-0532

e-ISSN

—

Svazek periodika

27

Číslo periodika v rámci svazku

45

Stát vydavatele periodika

SK - Slovenská republika

Počet stran výsledku

6

Strana od-do

107-112

Kód UT WoS článku

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EID výsledku v databázi Scopus

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