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Gaussian kernel smooth regression with topology learning neural networks and Python implementation
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DOI：10.1016/j.neucom.2017.01.051.png)
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Abstract
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.
Keywords:
Kernel density estimation
Semi-supervised regression
Python
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