Loss Functions for Clustering in Multi-instance Learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00533916" target="_blank" >RIV/67985807:_____/20:00533916 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21230/20:00348526 RIV/68407700:21340/20:00348526
Result on the web
<a href="http://ceur-ws.org/Vol-2718/paper05.pdf" target="_blank" >http://ceur-ws.org/Vol-2718/paper05.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Loss Functions for Clustering in Multi-instance Learning
Original language description
Multi-instance learning belongs to one of recently fast developing areas of machine learning. It is a supervised learning method and this paper reports research into its unsupervised counterpart, multi-instance clustering. Whereas traditional clustering clusters points, multiinstance clustering clusters bags, i.e. multisets of points or of other kinds of objects. The paper focuses on the problem of loss functions for clustering. Three sophisticated loss functions used for clustering of points, contrastive predictive coding, triplet loss and magnet loss, are elaborated for multi-instance clustering. Finally, they are compared on 18 benchmark datasets, as well as on a real-world dataset.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 20th Conference Information Technologies - Applications and Theory
ISBN
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ISSN
1613-0073
e-ISSN
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Number of pages
10
Pages from-to
137-146
Publisher name
Technical University & CreateSpace Independent Publishing
Place of publication
Aachen
Event location
Oravská Lesná
Event date
Sep 18, 2020
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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