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    Introducimos un marco de Aprendizaje Incremental en Línea (IOL) para Redes Neuronales Aleatorizadas (Red Neuronal Aleatorizada) para superar los desafíos en el aprendizaje continuo. Este marco mejora el rendimiento y reduce el arrepentimiento, especialmente con la regularización hacia adelante.

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    Área de la Ciencia:

    • Inteligencia Artificial
    • Aprendizaje Automático
    • Aprendizaje Profundo

    Sus antecedentes:

    • El aprendizaje en línea para redes neuronales profundas enfrenta problemas como actualizaciones retrasadas, altos costos y olvido catastrófico.
    • Los métodos existentes a menudo requieren reentrenamiento retrospectivo, lo que dificulta la toma de decisiones en tiempo real.

    Objetivo del estudio:

    • Proponer un marco novedoso de Aprendizaje Incremental en Línea (IOL) para Redes Neuronales Aleatorizadas (Red Neuronal Aleatorizada).
    • Permitir la toma de decisiones progresiva e inmediata y la mejora continua del rendimiento en escenarios en línea.

    Principales métodos:

    • Desarrolló un marco IOL para Redes Neuronales Aleatorizadas, que incluye IOL con regularización de cresta (-R) e IOL con regularización hacia adelante (-F).
    • Derivó algoritmos incrementales para -R/-F en flujos de lotes no estacionarios con actualizaciones recursivas de pesos y tasas de aprendizaje variables.
    • Derivó teóricamente límites de arrepentimiento acumulativo relativo para aprendices -R/-F bajo supuestos adversarios.

    Principales resultados:

    • Ambos marcos -R y -F evitan el reentrenamiento retrospectivo y el olvido catastrófico.
    • -F demostró un rendimiento de aprendizaje mejorado al utilizar datos futuros no etiquetados y reducir los arrepentimientos en línea en comparación con -R.
    • El análisis teórico y la validación empírica mostraron una aceleración superior del aprendizaje en línea y límites de arrepentimiento reducidos con -F.

    Conclusiones:

    • Los marcos IOL propuestos para Redes Neuronales Aleatorizadas son efectivos para el aprendizaje continuo y el análisis.
    • La regularización hacia adelante (-F) ofrece ventajas significativas sobre la regularización de cresta (-R) en escenarios de aprendizaje en línea, particularmente para la predicción de series de tiempo a largo plazo y el aprendizaje continuo.