Comparison of principal components analysis with diffusion maps on toy data sets and a molecular simulation trajectory
-
Updated
Feb 1, 2017 - Fortran
Comparison of principal components analysis with diffusion maps on toy data sets and a molecular simulation trajectory
University of Washington: CSE 446 (WIN '17) Machine Learning
Information Retrieval in High Dimensional Data (class deliverables)
My first steps to becoming AI engineer :)
packer identification tool using SVM
This repository contains the projects and homework from CSC 591 Graph Data Mining
A perceptron optimized with Numba that makes use of dual coordinates and kernel functions instead of classical dot product
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
To deal with non-linearly separable we use SVM's Kernel Trick which maps data to higher dimension!
Gaussian Processes for Machine Learning
This is a homework about supervised learning and kernel trick for machine learning course @ FUM.
Exploring meachine learning techniques and algorithms. Including clustering algorithms, perceptron and, more.
This repository contains my projects in the context of the "Computational Intelligence" course (held at FUM).
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.
simple classifier with perceptron (university assignment)
PyTorch implementation of "Kernel Neural Optimal Transport" (ICLR 2023)
A multiclass Support Vector Machine that supports kernel tricks has been implemented by solving the SVM quadratic programming problem.
Implemented a multi-class SVM that supports multi-core parallel computing
Support Vector Machine-related projects
Add a description, image, and links to the kernel-trick topic page so that developers can more easily learn about it.
To associate your repository with the kernel-trick topic, visit your repo's landing page and select "manage topics."