基于粒子群优化的条件概率神经网络
Particle Swarm Optimization Based Algorithm for Conditional Probability Neural Network Learning
DOI: 10.12677/AIRR.2016.51002, PDF, HTML, XML,  被引量 下载: 2,504  浏览: 7,325  国家自然科学基金支持
作者: 徐骏捷, 江 敏：厦门大学福建省仿脑智能系统重点实验室，福建 厦门
关键词: 年龄估计；标签分布学习；条件概率神经网络；粒子群优化；Age Estimation； Label Distribution Learning； Conditional Probability Neural Network； Particle Swarm Optimization
摘要: 条件概率神经网络在进行模式分类时具有独特的优势，然而如何对其进行有效的训练，从而找到最优参数却是一个困难的问题。在考虑条件概率神经网络的结构特点之后，本文提出了一种基于粒子群优化的条件概率神经网络的训练方法。我们将这种基于粒子群优化的条件概率神经网络用于人脸年龄估计，实验结果表明这种网络能够显著地提高识别的准确率。
Abstract: Conditional probability neural network (CPNN) has special advantage in pattern classification problems. However, how to find the optimal parameters of the CPNN to achieve better perfor-mance is an extraordinary challenge. Considering the structure feature of CPNN, we proposed a new training method based on particle swarm optimization (PSO). This method utilizes PSO to optimize the structure of CPNN and label distributions by introducing Hellinger distance between different label distributions. We applied the improved CPNN on facial age estimation. The experimental results showed that this network could increase recognition accuracy significantly.
文章引用：徐骏捷, 江敏. 基于粒子群优化的条件概率神经网络[J]. 人工智能与机器人研究, 2016, 5(1): 13-22. http://dx.doi.org/10.12677/AIRR.2016.51002

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