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Migration is long-range, seasonal movement from one region or habitat to another. This common strategy, carried out by many different organisms around the world, is an adaptive response that typically corresponds to changes in an organism’s environment, like resource availability or climate. Migrations can involve huge groups of thousands of animals as well as single individuals traveling alone and can range from thousands of kilometers to just a few hundred meters.
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Este estudio presenta un método de conjunto adaptativo que utiliza la optimización de manadas de elefantes (EHO) para mejorar la precisión de la clasificación de imágenes con datos limitados. El novedoso enfoque mejora la selección de clasificadores para un mejor rendimiento en conjuntos de datos como GAIT y ODIR-5K.

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optimización de manadas de elefantesaprendizaje por transferenciaclasificación de imágenesmétodo de conjunto adaptativoaprendizaje profundo

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Área de la Ciencia:

  • Ciencias de la Computación
  • Inteligencia Artificial
  • Aprendizaje Automático

Sus antecedentes:

  • El aprendizaje por transferencia es crucial para la clasificación de imágenes con datos de entrenamiento limitados.
  • Los métodos de conjunto existentes se pueden mejorar para una mayor eficiencia y precisión.

Objetivo del estudio:

  • Proponer un novedoso método de conjunto adaptativo para la clasificación de imágenes utilizando aprendizaje por transferencia.
  • Mejorar el rendimiento de la clasificación de imágenes optimizando la selección de clasificadores con la optimización de manadas de elefantes (EHO).

Principales métodos:

  • Construcción de múltiples clasificadores utilizando aprendizaje por transferencia.
  • Combinación de salidas probabilísticas en una única matriz de características.
  • Empleo de la optimización de manadas de elefantes (EHO) para seleccionar el subconjunto más efectivo de clasificadores para el conjunto.

Principales resultados:

  • El método de conjunto adaptativo propuesto basado en EHO mejora significativamente la precisión de la clasificación de imágenes.
  • El método mejora la eficiencia al reducir la redundancia en la selección de clasificadores.
  • Los resultados experimentales en los conjuntos de datos GAIT y ODIR-5K demuestran un rendimiento superior en comparación con las estrategias de conjunto clásicas.

Conclusiones:

  • El novedoso enfoque de conjunto adaptativo aprovecha eficazmente el aprendizaje por transferencia y la EHO para una clasificación de imágenes superior.
  • Este método ofrece una solución robusta para escenarios con datos de entrenamiento limitados, superando a las técnicas de conjunto tradicionales.