Hardware-accelerated distance metrics and similarity measures for high-dimensional data

Similarity and distance measures for clustering and record linkage applications in R

KNN using brute force and ball trees implemented in Python/Cython

The Python 3 library for Multi-Criteria Decision Analysis.

PyTorch implementations of the beta divergence loss.

Lab Experiments under Lab component of CSE3018 - Content-based Image and Video Retrieval course at Vellore Institute of Technology, Chennai

Python 3 library for Multi-Criteria Decision Analysis based on distance metrics, providing twenty different distance metrics.

Distance metrics are one of the most important parts of some machine learning algorithms, supervised and unsupervised learning, it will help us to calculate and measure similarities between numerical values expressed as data points

Repository on Approximate Bayesian Computation and the different distance metrics which can be implemented.

🪓 Predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset

DTW(Dynamic Time Warping) & Subsequence-DTW Python Module

🌼 Classify the different species of the Iris flower.

Classification model to categorize clothing items into distinct classes

Classification of IRIS Dataset using various distance metrics.

This Jupyter Notebook demonstrates the implementation of a K-Nearest Neighbors (KNN) algorithm using the concept of nearest neighbors without using direct classifiers. It also includes exploratory data analysis (EDA) and comparison of three classifiers.

Practice Material