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Data identification model by domain symmetry

Om Data identification model by domain symmetry

Data identification algorithms are useful models in areas of research and technological development where pattern recognition functions are required to classify data within a definition; the domain symmetry model is a highly efficient Machine Learning algorithm for data identification, capable of classifying images and sounds with a low amount of training data, besides being a model capable of extrapolating to the identification of other types of data thanks to its modeling stage where a contextualization on the nature of the data is applied for the training of the system.Throughout this book the automation of this model is developed and implemented; a model of accessible implementation, with a low computational cost, since it requires a very low amount of training data, fast, versatile in multiple applications and highly efficient.

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  • Språk:
  • Engelsk
  • ISBN:
  • 9786206598350
  • Bindende:
  • Paperback
  • Sider:
  • 68
  • Utgitt:
  • 25. oktober 2023
  • Dimensjoner:
  • 150x5x220 mm.
  • Vekt:
  • 119 g.
  • BLACK NOVEMBER
  Gratis frakt
Leveringstid: 2-4 uker
Forventet levering: 12. desember 2024

Beskrivelse av Data identification model by domain symmetry

Data identification algorithms are useful models in areas of research and technological development where pattern recognition functions are required to classify data within a definition; the domain symmetry model is a highly efficient Machine Learning algorithm for data identification, capable of classifying images and sounds with a low amount of training data, besides being a model capable of extrapolating to the identification of other types of data thanks to its modeling stage where a contextualization on the nature of the data is applied for the training of the system.Throughout this book the automation of this model is developed and implemented; a model of accessible implementation, with a low computational cost, since it requires a very low amount of training data, fast, versatile in multiple applications and highly efficient.

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