KNN using brute force and ball trees implemented in Python/Cython
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Updated
Mar 26, 2019 - Python
KNN using brute force and ball trees implemented in Python/Cython
The Python 3 library for Multi-Criteria Decision Analysis.
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
Python 3 library for Multi-Criteria Decision Analysis based on distance metrics, providing twenty different distance metrics.
Lab Experiments under Lab component of CSE3018 - Content-based Image and Video Retrieval course at Vellore Institute of Technology, Chennai
DTW(Dynamic Time Warping) & Subsequence-DTW Python Module
🌼 Classify the different species of the Iris flower.
🪓 Predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset
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.
Classification model to categorize clothing items into distinct classes
Classification of IRIS Dataset using various distance metrics.
Similarity and distance measures for clustering and record linkage applications in R
Repository on Approximate Bayesian Computation and the different distance metrics which can be implemented.
Practice Material
PyTorch implementations of the beta divergence loss.
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