kernel-trick

Information Retrieval in High Dimensional Data (class deliverables)

PyTorch implementation of "Kernel Neural Optimal Transport" (ICLR 2023)

Comparison of principal components analysis with diffusion maps on toy data sets and a molecular simulation trajectory

Multi-Shot Approximation of Discounted Cost MDPs

To deal with non-linearly separable we use SVM's Kernel Trick which maps data to higher dimension!

A perceptron optimized with Numba that makes use of dual coordinates and kernel functions instead of classical dot product

University of Washington: CSE 446 (WIN '17) Machine Learning

This repository contains my projects in the context of the "Computational Intelligence" course (held at FUM).

packer identification tool using SVM

This repository contains the projects and homework from CSC 591 Graph Data Mining

My first steps to becoming AI engineer :)

📊 Predicting stock price direction using Support Vector Machine (SVM), with a focus on both theoretical foundations and practical implementation. Includes evaluation via confusion matrix, classification report, and heatmap visualization.

In this project I developed a program that generates nonlinearly separable data. This data is mapped into a higher dimension using a kernel trick where the data becomes linearly separable

Hard Margin·Soft Margin·Kernel Trick을 포함한 Support Vector Machine(SVM) 구현 (GIST EC4213)

Machine Learning Algorithms

Exploring meachine learning techniques and algorithms. Including clustering algorithms, perceptron and, more.

Interactive visualization of Support Vector Machines (SVM), Support Vectors, Margins, Decision Boundaries, and the Kernel Trick using Scikit-Learn and Plotly.

simple classifier with perceptron (university assignment)

Understanding of Support Vector Machines (SVMs) and their implementations. Covers linear and non-linear SVM, also explains how to use the "kernel trick" to handle non-linearly separable data.