Deep Unfolding Network for Image Super-Resolution (CVPR, 2020) (PyTorch)

Fast Incremental Support Vector Data Description implemented in Python

classify mnist datasets using ridge regression, optimize the algorithem with SGD, stochastic dual coordinate ascent, and mini-batching

Implementation of the algorithm from "Fast training of Support Vector Machines with Gaussian kernel" (Fischetti, 2016)

A nonlinear classifier for categorizing shoes using machine learning

SVM with Gaussian kernel implementation for spam classification problem using numpy

Classification of a radially seperated dataset using SVM with RBF kernel using CVXOPT

PyTorch implementation of important functions for WAIL and GMMIL

This project implements Support Vector Regression (SVR) to predict the salary of an employee based on their position level. The script uses a dataset that contains position levels and corresponding salaries, applying feature scaling to improve the performance of the SVR model. The results are visualized to show how well the model fits the data.

Classification of wine quality using a hard_parzen and a soft_parzen with gaussian kernel models

Solving the Character recognition problem as an SVM optimization problem using CVXOPT