找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: Advances in Self-Organizing Maps; 7th International Wo José C. Príncipe,Risto Miikkulainen Conference proceedings 2009 Springer-Verlag Berl

[复制链接]
楼主: Hallucination
发表于 2025-3-28 15:18:47 | 显示全部楼层
Fault Prediction in Aircraft Engines Using Self-Organizing Maps,s. The goal is to ensure a proper operation of the engines, in all conditions, with a zero probability of failure, while taking into account aging. The fact that the same engine is sometimes used on several aircrafts has to be taken into account too..The maintenance can be improved if an efficient p
发表于 2025-3-28 20:02:34 | 显示全部楼层
Incremental Figure-Ground Segmentation Using Localized Adaptive Metrics in LVQ,ss. In this paper we present an incremental learning scheme in the context of figure-ground segmentation. In presence of local adaptive metrics and supervised noisy information we use a parallel evaluation scheme combined with a local utility function to organize a learning vector quantization (LVQ)
发表于 2025-3-29 00:33:03 | 显示全部楼层
Application of Supervised Pareto Learning Self Organizing Maps and Its Incremental Learning,tors and applied SP-SOM to the biometric authentication system which uses multiple behavior characteristics as feature vectors. In this paper, we examine performance of SP-SOM for the generic classification problem using iris data set. Furthermore, we propose the incremental learning algorithm for S
发表于 2025-3-29 05:47:19 | 显示全部楼层
发表于 2025-3-29 08:47:37 | 显示全部楼层
发表于 2025-3-29 14:01:25 | 显示全部楼层
发表于 2025-3-29 17:39:55 | 显示全部楼层
Cartograms, Self-Organizing Maps, and Magnification Control,rts with a brief explanation of what a cartogram is, how it can be used, and what sort of metrics can be used to assess its quality. The methodology for creating a cartogram with a SOM is then presented together with an explanation of how the magnification effect can be compensated in this case by p
发表于 2025-3-29 22:14:54 | 显示全部楼层
ViSOM for Dimensionality Reduction in Face Recognition,OM) and growing ViSOM (gViSOM) are two recently proposed variants for a more faithful, metric-based and direct data representation. They learn local quantitative distances of data by regularizing the inter-neuron contraction force while capturing the topology and minimizing the quantization error. I
发表于 2025-3-30 00:57:21 | 显示全部楼层
发表于 2025-3-30 07:09:35 | 显示全部楼层
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-5-18 14:18
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表