https://github.com/renkun-ken/knn

A k-NN based non-parametric non-linear non-stationary time series predictor
https://github.com/renkun-ken/knn

Last synced: 27 days ago
JSON representation

A k-NN based non-parametric non-linear non-stationary time series predictor

• Host: GitHub
• URL: https://github.com/renkun-ken/knn
• Owner: renkun-ken
• Created: 2013-12-28T06:01:21.000Z (almost 11 years ago)
• Default Branch: master
• Last Pushed: 2014-01-15T02:19:24.000Z (almost 11 years ago)
• Last Synced: 2023-08-06T18:11:25.408Z (over 1 year ago)
• Language: R
• Homepage:
• Size: 875 KB
• Stars: 2
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
README

===========================

Introduction

---------------------------

In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method for classification and regression that predicts objects' "values" or class memberships based on the k closest training examples in the feature space. k-NN is a type of instance-based learning, or lazy learning where the function is only approximated locally and all computation is deferred until classification. The k-nearest neighbor algorithm is amongst the simplest of all machine learning algorithms: an object is classified by a majority vote of its neighbors, with the object being assigned to the class most common amongst its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor.

Motivation

---------------------------

References

---------------------------

https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm

https://en.wikipedia.org/wiki/Nearest_neighbor_search