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Towards increasing predictability of machine-learning research
<!--HTML--><p align="justify"> Dealing with system complexity is common problem for wide spectre of companies. It is always the case when people use machine learning for their needs. In Yandex as search engine company we are working with rapidly growing datasets, variety of dat...
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Lenguaje: | eng |
Publicado: |
2013
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Acceso en línea: | http://cds.cern.ch/record/1601031 |
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author | Vorozhtsov, Artem Ustyuzhanin, Andrey |
author_facet | Vorozhtsov, Artem Ustyuzhanin, Andrey |
author_sort | Vorozhtsov, Artem |
collection | CERN |
description | <!--HTML--><p align="justify">
Dealing with system complexity is common problem for wide spectre of
companies. It is always the case when people use machine learning for their
needs. In Yandex as search engine company we are working with rapidly
growing datasets, variety of data and different set of quality metrics.
Usually research results in such environments are difficult to predict and
tend to take unpredictable amount of time.
</p>
<p align="justify">
This talk describes several scenarios from Yandex everyday life that rely on
machine learning techniques focusing on principles and instruments that help
making research results more predictable both in terms of time required and
quality of obtained results.</p> |
id | cern-1601031 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2013 |
record_format | invenio |
spelling | cern-16010312022-11-02T22:30:06Zhttp://cds.cern.ch/record/1601031engVorozhtsov, ArtemUstyuzhanin, AndreyTowards increasing predictability of machine-learning researchTowards increasing predictability of machine-learning researchComputing Seminar<!--HTML--><p align="justify"> Dealing with system complexity is common problem for wide spectre of companies. It is always the case when people use machine learning for their needs. In Yandex as search engine company we are working with rapidly growing datasets, variety of data and different set of quality metrics. Usually research results in such environments are difficult to predict and tend to take unpredictable amount of time. </p> <p align="justify"> This talk describes several scenarios from Yandex everyday life that rely on machine learning techniques focusing on principles and instruments that help making research results more predictable both in terms of time required and quality of obtained results.</p>oai:cds.cern.ch:16010312013 |
spellingShingle | Computing Seminar Vorozhtsov, Artem Ustyuzhanin, Andrey Towards increasing predictability of machine-learning research |
title | Towards increasing predictability of machine-learning research |
title_full | Towards increasing predictability of machine-learning research |
title_fullStr | Towards increasing predictability of machine-learning research |
title_full_unstemmed | Towards increasing predictability of machine-learning research |
title_short | Towards increasing predictability of machine-learning research |
title_sort | towards increasing predictability of machine-learning research |
topic | Computing Seminar |
url | http://cds.cern.ch/record/1601031 |
work_keys_str_mv | AT vorozhtsovartem towardsincreasingpredictabilityofmachinelearningresearch AT ustyuzhaninandrey towardsincreasingpredictabilityofmachinelearningresearch |