Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Alexander Engelsberger"'
Publikováno v:
Entropy, Vol 25, Iss 3, p 540 (2023)
In the field of machine learning, vector quantization is a category of low-complexity approaches that are nonetheless powerful for data representation and clustering or classification tasks. Vector quantization is based on the idea of representing a
Externí odkaz:
https://doaj.org/article/a65104799452454b8f007e2483443370
Publikováno v:
Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2eeacfbbba1989cd14e533ff258a7456
https://doi.org/10.5220/0011644000003414
https://doi.org/10.5220/0011644000003414
Publikováno v:
Neural Computing and Applications. 34:79-88
Prototype-based models like the Generalized Learning Vector Quantization (GLVQ) belong to the class of interpretable classifiers. Moreover, quantum-inspired methods get more and more into focus in machine learning due to its potential efficient compu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aa09aa6a86827e6187915f49940fceb9
https://doi.org/10.1007/s00521-020-05517-y
https://doi.org/10.1007/s00521-020-05517-y
Publikováno v:
Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization ISBN: 9783031154430
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8a424bc03ce18cefc6640704f169bd4b
https://doi.org/10.1007/978-3-031-15444-7_7
https://doi.org/10.1007/978-3-031-15444-7_7
Publikováno v:
Artificial Intelligence and Soft Computing ISBN: 9783030879853
ICAISC (1)
ICAISC (1)
The paper demonstrates how to realize neural vector quantizers by means of quantum computing approaches. Particularly, we consider self-organizing maps and the neural gas vector quantizer for unsupervised learning as well as generalized learning vect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4370b6c297fe9a24b5ad7f197c4fa13c
https://doi.org/10.1007/978-3-030-87986-0_22
https://doi.org/10.1007/978-3-030-87986-0_22