Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Jensun Ravichandran"'
Publikováno v:
Neurocomputing. 502:27-36
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f883ee5359882f63bd37e92bc5bae385
https://doi.org/10.1016/j.neucom.2022.06.035
https://doi.org/10.1016/j.neucom.2022.06.035
Publikováno v:
Neural Information Processing ISBN: 9783031301070
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::27fc46e0240479b022fdad21dd670515
https://doi.org/10.1007/978-3-031-30108-7_32
https://doi.org/10.1007/978-3-031-30108-7_32
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:
Neurocomputing. 403:121-132
Dropout and DropConnect are useful methods to prevent multilayer neural networks from overfitting. In addition, it turns out that these tools can also be used to estimate the stability of networks regarding disturbances. Prototype based networks gain
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::20ae9de72461efed3a69f1a09fc861a1
https://doi.org/10.1016/j.neucom.2019.12.131
https://doi.org/10.1016/j.neucom.2019.12.131
Publikováno v:
ESANN 2021 proceedings.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d186710525776e6665a7cccdc67ba17b
https://doi.org/10.14428/esann/2021.es2021-90
https://doi.org/10.14428/esann/2021.es2021-90
Publikováno v:
Advances in Intelligent Systems and Computing ISBN: 9783030196417
WSOM
WSOM
An appropriate choice of the activation function plays an important role for the performance of (deep) multilayer perceptrons (MLP) in classification and regression learning. Usually, these activations are applied to all perceptron units in the netwo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8ddbbbef6601b12d0cfd52dc0b47b8f3
https://doi.org/10.1007/978-3-030-19642-4_18
https://doi.org/10.1007/978-3-030-19642-4_18