Kholod et al., 2021 - Google Patents

Parallelization of the self-organized maps algorithm for federated learning on distributed sources

Kholod et al., 2021

Document ID
11621709721872705272
Author
Kholod I
Rukavitsyn A
Paznikov A
Gorlatch S
Publication year
Publication venue
The Journal of Supercomputing

External Links

Snippet

This paper describes a formally based approach for parallelizing the Kohonen algorithm used for the federated learning process in a special kind of neural networks—Self- Organizing Maps. Our approach enables executing the parallel algorithm version on the …
Continue reading at link.springer.com (other versions)

Classifications

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    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30587—Details of specialised database models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
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    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
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    • G06K9/6251—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on a criterion of topology preservation, e.g. multidimensional scaling, self-organising maps
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    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
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