Suppr超能文献

核贝叶斯 ART 和 ARTMAP。

Kernel Bayesian ART and ARTMAP.

机构信息

Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, Sakai-Shi, Osaka 599-8531, Japan.

Faculty of Computer Science and Information Technology, University of Malaya, Lembah Pantai, 50603 Kuala Lumpur, Malaysia.

出版信息

Neural Netw. 2018 Feb;98:76-86. doi: 10.1016/j.neunet.2017.11.003. Epub 2017 Nov 10.

Abstract

Adaptive Resonance Theory (ART) is one of the successful approaches to resolving "the plasticity-stability dilemma" in neural networks, and its supervised learning model called ARTMAP is a powerful tool for classification. Among several improvements, such as Fuzzy or Gaussian based models, the state of art model is Bayesian based one, while solving the drawbacks of others. However, it is known that the Bayesian approach for the high dimensional and a large number of data requires high computational cost, and the covariance matrix in likelihood becomes unstable. This paper introduces Kernel Bayesian ART (KBA) and ARTMAP (KBAM) by integrating Kernel Bayes' Rule (KBR) and Correntropy Induced Metric (CIM) to Bayesian ART (BA) and ARTMAP (BAM), respectively, while maintaining the properties of BA and BAM. The kernel frameworks in KBA and KBAM are able to avoid the curse of dimensionality. In addition, the covariance-free Bayesian computation by KBR provides the efficient and stable computational capability to KBA and KBAM. Furthermore, Correntropy-based similarity measurement allows improving the noise reduction ability even in the high dimensional space. The simulation experiments show that KBA performs an outstanding self-organizing capability than BA, and KBAM provides the superior classification ability than BAM, respectively.

摘要

自适应共振理论(ART)是解决神经网络中“可塑性-稳定性困境”的成功方法之一,其监督学习模型 ARTMAP 是一种强大的分类工具。在几种改进方法中,例如基于模糊或高斯的模型,最先进的模型是基于贝叶斯的模型,同时解决了其他模型的缺点。然而,众所周知,贝叶斯方法对于高维数据和大量数据需要高计算成本,并且似然中的协方差矩阵变得不稳定。本文通过将核贝叶斯规则(KBR)和相关熵诱导度量(CIM)分别集成到贝叶斯 ART(BA)和 ARTMAP(BAM)中,引入核贝叶斯 ART(KBA)和 ARTMAP(KBAM),同时保持 BA 和 BAM 的特性。KBA 和 KBAM 中的核框架能够避免维度灾难。此外,KBR 的无协方差贝叶斯计算为 KBA 和 KBAM 提供了高效稳定的计算能力。此外,基于相关熵的相似性度量允许提高降噪能力,即使在高维空间中也是如此。仿真实验表明,KBA 比 BA 具有出色的自组织能力,KBAM 比 BAM 具有更好的分类能力。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验