Using the ES algorithm to train RBF-network and implement regression and classification algorithms on the dataset
-
Updated
Aug 25, 2020 - Python
Using the ES algorithm to train RBF-network and implement regression and classification algorithms on the dataset
evolutionary-based approach in RBF neural network training
SPPU - BE ENTC (2015 Pattern) - Elective III
Kaggle Machine Learning Competition Project : To classify activities into one of the six activities performed by individuals by reading the inertial sensors data collected using Smartphone.
Using data mining techniques to predict if the organization is prone to bankruptcy using the data with 250 records and 6 nominal attributes per record. Machine learning techniques used: Linear and non-linear SVM, Decision Tree Classifier, Gaussian Naive Bayes.
Two pattern classification problem using Radial Basis Functions (RBF) Neural Networks, with center vectors selected via self-organizing map (SOM) neural networks.
This repository describes the application of numerical Collocation method in Machine Learning as a Supervised learning algorithm
Add a description, image, and links to the rbf-classifier topic page so that developers can more easily learn about it.
To associate your repository with the rbf-classifier topic, visit your repo's landing page and select "manage topics."