Fast radial basis function interpolation for large scale data

A custom Radial Basis Function (RBF) neural network is implemented to predict gold prices using time series data.

SVM with different kernels and decision trees

Classification of EEG signals using different AI/ML models

A cross-platform AR Visualizer utilizing a Radial-kernel-trained Support Vector Machine (SVM) model to generate interactive 3D model to understand machine learning concepts

This paper proposes an alternative data-driven hap- tic modeling method of homogeneous deformable objects based on a CatBoost approach – a variant of gradient boosting machine learning approach. In this approach, decision trees are trained sequentially to learn the required mapping function for modeling the objects.

Second homework for the Reinforcement Learning course

Implemented Multilayer Perceptron (MLP) and Radial Basis Function (RBF) neural networks from scratch in Python and used ResNet-34 as a feature extractor. Evaluated and compared the classification accuracy of the two networks on the CIFAR-10 dataset.

Implemented using Linear SVM(SVC) and Non-Linear SVM(RBF). ML ASSIGNMENT 2 => Q2

Javascript Bitcoin Wallet. Supports Multisig, Stealth, HD, SegWit, Bech32, Time Locked Addresses, RBF and more!

Classifier for CIFAR-10. Grayscaling, HOG, PCA, and RBF SVM. 62% test accuracy. Walkthrough on YouTube: https://youtu.be/gmTweV0eHhk

[ICCV 2021] Authors official PyTorch implementation of the "WarpedGANSpace: Finding non-linear RBF paths in GAN latent space".

This repository consists of the Analysis and ML training of the MNIST_Digit_recognizer on kaggle

GPU-Accelerated Radial Basis Function (RBF) Interpolation in PyTorch

Hesabu

Train Perceptrons, MLPs, and RBFs by applying Delta rule. This repo is result of our joint work (Ilaha Manafova & I) for the "Technical Neural Networks" course held at the University of Bonn.

RBF (Radial Basis Function) for classification

An RBF network implementation for interpolation