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System and Method for Building Ensemble Models Using Competitive Reinforcement Learning

机译:使用竞争力强化学习构建集合模型的系统和方法

摘要

This disclosure relates to method and system for building ensemble models using competitive reinforcement learning (CRL). The method may include creating a plurality of clusters, each including a set of predictive models forming the ensemble model. For each of the plurality of clusters, the method may further include initializing each of the set of predictive models with random values for a set of first parameters to obtain a first accuracy score, categorizing each of the set of predictive models into a first associated category based on a first accuracy score, calculating a first reward for each of the set of predictive models based on the first associated category, and determining a set of second parameters for each of the set of predictive models using CRL to obtain a second accuracy score.
机译:本公开涉及使用竞争强度学习(CRL)构建集合模型的方法和系统。 该方法可以包括创建多个簇,每个聚类包括形成集合模型的一组预测模型。 对于多个群集中的每一个,该方法还可以包括以一组第一参数用随机值初始化每组预测模型,以获得第一精度分数,将每个集合的预测模型分类为第一相关类别 基于第一精度得分,基于第一相关联的类别计算每个预测模型集合的第一奖励,并使用CRL确定一组预测模型集的一组第二参数以获得第二精度分数。

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