51 Open source projects that are alternatives of or similar to KernelKnn

Explaining the output of machine learning models with more accurately estimated Shapley values

Machine learning algorithm such as KNN,Naive Bayes,Logistic Regression,SVM,Decision Trees,Random Forest,k means and Truncated SVD on amazon fine food review

机器学习教程，本教程包含基于numpy、sklearn与tensorflow机器学习，也会包含利用spark、flink加快模型训练等用法。本着能够较全的引导读者入门机器学习。

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🏆 A Comparative Study on Handwritten Digits Recognition using Classifiers like K-Nearest Neighbours (K-NN), Multiclass Perceptron/Artificial Neural Network (ANN) and Support Vector Machine (SVM) discussing the pros and cons of each algorithm and providing the comparison results in terms of accuracy and efficiecy of each algorithm.

Python Clustering Algorithms

Official code for "Mean Shift for Self-Supervised Learning"

Python implementation of supervised PCA, supervised random projections, and their kernel counterparts.

Master's degree project: Development of a trading algorithm which uses supervised machine learning classification techniques to generate buy/sell signals

R Package: Regularized Principal Component Analysis for Spatial Data

Mobile(iOS) Text-to-Image search powered by multimodal semantic representation models(e.g., OpenAI's CLIP). Accepted at NAACL 2022.

UAI 2015. Kernel-based just-in-time learning for expectation propagation

Machine Learning for Spot Prices

Gaussian mixture models, k-means, mini-batch-kmeans and k-medoids clustering

FUKinect-Fall dataset was created using Kinect V1. The dataset includes walking, bending, sitting, squatting, lying and falling actions performed by 21 subjects between 19-72 years of age.

Fast Unit Root Tests and OLS regression in C++ with wrappers for R and Python

A document covering machine learning basics. 🤖📊

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An Interactive Approach to Understanding Supervised Learning Algorithms

No description or website provided.

A python package for graph kernels, graph edit distances, and graph pre-image problem.

In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).

Animated Visualizations of Popular Machine Learning Algorithms

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NeurIPS 2016. Linear-time interpretable nonparametric two-sample test.

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Implementing Content based and Collaborative filtering(with KNN, Matrix Factorization and Neural Networks) in Python

Formed trajectories of sets of points.Experimented on finding similarities between trajectories based on DTW (Dynamic Time Warping) and LCSS (Longest Common SubSequence) algorithms.Modeled trajectories as strings based on a Grid representation.Benchmarked KNN, Random Forest, Logistic Regression classification algorithms to classify efficiently t…

NeurIPS 2018. Linear-time model comparison tests.

This project is to convert PortraitFCN+ (by Xiaoyong Shen) from Matlab to Tensorflow, then refine the outputs from it (converted to a trimap) using KNN and ResNet, supervised by Richard Berwick.

To identify the driver's drowsiness based on real-time camera image and image processing techniques. 졸음운전 감지 시스템. OpenCV

A collection of important graph embedding, classification and representation learning papers with implementations.

This is the page for the book Digital Signal Processing with Kernel Methods.

A Matlab benchmarking toolbox for kernel adaptive filtering

Machine learning, in numpy

⚡机器学习实战（Python3）：kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归

Code for the blog post Nearest Neighbors with Keras and CoreML

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Simple Implementation of Network Intrusion Detection System. KddCup'99 Data set is used for this project. kdd_cup_10_percent is used for training test. correct set is used for test. PCA is used for dimension reduction. SVM and KNN supervised algorithms are the classification algorithms of project. Accuracy : %83.5 For SVM , %80 For KNN

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