Cognitive Driven Multilayer Self-Paced Learning with Misclassified Samples
Cognitive Driven Multilayer Self-Paced Learning with Misclassified Samples
复制标题
认知驱动的多层自定进度学习与错误分类的样本
DOI：
10.1155/2019/8127869
复制
发表时间：
2019
期刊：
影响因子：
2.3
通讯作者：
Donghai Guan
中科院分区：
文献类型：
--
作者：
Qi Zhu;Ning Yuan;Donghai Guan
In recent years, self‐paced learning (SPL) has attracted much attention due to its improvement to nonconvex optimization based machine learning algorithms. As a methodology introduced from human learning, SPL dynamically evaluates the learning difficulty of each sample and provides the weighted learning model against the negative effects from hard‐learning samples. In this study, we proposed a cognitive driven SPL method, i.e., retrospective robust self‐paced learning (R2SPL), which is inspired by the following two issues in human learning process: the misclassified samples are more impressive in upcoming learning, and the model of the follow‐up learning process based on large number of samples can be used to reduce the risk of poor generalization in initial learning phase. We simultaneously estimated the degrees of learning‐difficulty and misclassified in each step of SPL and proposed a framework to construct multilevel SPL for improving the robustness of the initial learning phase of SPL. The proposed method can be viewed as a multilayer model and the output of the previous layer can guide constructing robust initialization model of the next layer. The experimental results show that the R2SPL outperforms the conventional self‐paced learning models in classification task.
