Gaussian kernel smooth regression with topology learning neural networks and Python implementation

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.