.png){kind=icon}
返回
Gaussian kernel smooth regression with topology learning neural networks and Python implementation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2017.01.051.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Topology learning neural networks such as Growing Neural Gas (GNG) and Self-Organizing Incremental Neural Network (SOINN) are online clustering methods. With GNG and SOINN implemented as basic learners, this software completes two machine learning tasks, namely density estimation and regression. A kernel density estimation framework is implemented to transform the topology learning neural networks into density estimation methods. Besides, a kernel smoother to implement supervised and semi supervised regression is devised. Moreover, the implemented frameworks can be used to transform other clustering methods into density estimation, supervised regression and semi-supervised regression. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
Kernel density estimation
Semi-supervised regression
Python
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods使用机器学习方法预测基本负荷运行的联合循环电厂的满负荷电功率输出
PREAI
分享收藏Modeling wine preferences by data mining from physicochemical properties通过物理化学特性的数据挖掘对葡萄酒的偏好进行建模
OAAI
OAAI分享收藏A Gaussian mixture framework for incremental nonparametric regression with topology learning neural networks.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏 没有更多内容
没有更多内容