Just a simple implementation of K-Nearest Neighbour algorithm.
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Updated
Sep 8, 2016 - Java
Just a simple implementation of K-Nearest Neighbour algorithm.
In this project, aim is to categorise movies into genres by analysing subtitles with machine learning techniques.
Inteligência Artificial (UTFPR)
This repository contains code for implementing kNN algorithm on KEEL (http://sci2s.ugr.es/keel/category.php?cat=clas) datasets using Python.
Main aim is to generate movie recommendations for a user, given the movie they already watched and the respective rating they gave for those movies
Achieved an accuracy of 90% in Handwritten Digit Recognition by implementing K-Nearest Neighbor(K-NN) algorithm on MNIST dataset (a database of several handwritten digits ) to recognize any handwritten digit.
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Created topic models with tf-idf for 142 unlabled news articles using the Stanford Core NLP library. With the resulting data, implemented clustering and classification algorithms (K-means and KNN) from scratch.
Artificial Intelligence Lab | Spring 2018
Execução dos códigos do Livro Machine Learning with R Packt
Uses KNN algorithm to predict user-movie rating
Implementasi algoritma kNN dengan Python
The project classifies characters (Hand-Written) by three algorithms Weighted kNN, Bayesian and SVM (using MATLAB R2016b and App Design GUI)
High Frequency Time series Anomaly Detection using Self Organizing Maps (SOM) which is based on Competitive Learning a variant of the Neural Networks using K Nearest Neighbors
A collection of machine learning models with python.
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Recommender System using movielens 100k dataset
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