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通过实用初始化策略,提高树突神经元模型的分类性能

Xiaohao Wen1,2, Mengchu Zhou2,3, Aiiad Albeshri4

  • 1Teachers College for Vocational and Technical Education, Guangxi Normal University, Guilin 541001, China.

Sensors (Basel, Switzerland)
|March 28, 2024
PubMed
概括

一种新的初始化方法提高了树突神经元模型 (DNM) 在高维数据上的性能. 这种简单,快速的技术为深度学习初始化提供了卓越的结果和见解.

关键词:
深度学习是一种深度学习.树突神经元模型的神经元模型初始化方法的初始化方法神经网络的神经网络的神经网络

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相关概念视频

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...