神经网络装配记忆模型框架下的ROC曲线：一些分析结果
ROC Curves Within the Framework of Neural Network Assembly Memory Model: Some Analytic Results
摘要：
基于卷积（Hamming）形式的最新神经网络装配记忆模型（NAMM），解析地导出了完整的两层自联想Hopfield网络的最优接收机工作特性（ROC）。考虑到明确的先验概率的替代假设的结构上的信息启动记忆痕迹检索和修改的ROC（mROCs，后验概率的正确召回与虚警概率）的方法进行了介绍。经验和计算的ROC（或mROC）的比较表明，它们在数量上一致，并以这种方式在适当的实验中使用的线索的强度可以估计。它已被发现，基本的ROC属性，这是一个实验结果的基础上的双过程模型的再认记忆可以解释在我们的单因素NAMM。
英文摘要：
On the basis of convolutional (Hamming) version of recent Neural Network Assembly Memory Model (NNAMM) for intact two-layer autoassociative Hopfield network optimal receiver operating characteristics (ROCs) have been derived analytically. A method of taking into account explicitly a priori probabilities of alternative hypotheses on the structure of information initiating memory trace retrieval and modified ROCs (mROCs, a posteriori probabilities of correct recall vs. false alarm probability) are introduced. The comparison of empirical and calculated ROCs (or mROCs) demonstrates that they coincide quantitatively and in this way intensities of cues used in appropriate experiments may be estimated. It has been found that basic ROC properties which are one of experimental findings underpinning dual-process models of recognition memory can be explained within our one-factor NNAMM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
