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The influence of kernel function on model performance.

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Author(s): Zhengyang Wang (4349938); Shufang Tian (803723)
Subject Terms:
Biotechnology; Science Policy; Space Science; Environmental Sciences not elsewhere classified; Biological Sciences not elsewhere classified; Information Systems not elsewhere classified; backpropagation neural network; feature extraction targets; hyperspectral image data; classifying lithological information; lithological information extraction; introducing deep learning; classification model based; deep learning; spatial information; proposed extraction; lithological spectrum; hyperspectral rock; %22">xlink ">; taking rocks; rock masses; results demonstrate; research values; research object; precisely distinguish; practical significances; kappa coefficient; isomorphism encountered; classification model; analysis models
Document Type:
still image
Language:
unknown

Additional Information
- Publication Date:
  2021
- Collection:
  Smithsonian Institution: Digital Repository
- Abstract:
  Note: Letters A–E denote the characteristics of alunite, kaolinite, montmorillonite, chalcedony, and alunite + kaolinite.
- Relation:
  https://figshare.com/articles/figure/The_influence_of_kernel_function_on_model_performance_/16814122
- Accession Number:
  10.1371/journal.pone.0254542.g008
- Online Access:
  https://doi.org/10.1371/journal.pone.0254542.g008
- Rights:
  CC BY 4.0
- Accession Number:
  edsbas.4C6BE26F

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