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  • **Density Estimation with K-Nearest Neighbors (KNN) ***[[K-Nearest Neighbors Density Estimation|Video slecture in English]] by Qi Wang <span style="colo
    10 KB (1,450 words) - 20:50, 2 May 2016
  • ...e the density for each point to be classified. Parzen window and K-nearest neighbors (KNN) are two of the famous non-parametric methods. Two common concerns abo ...><math>\widehat{\mathbf{\Sigma}} = \frac{1}{N}\sum_{k=1}^{N}(x_{k}-\mu)(x_{k}-\mu)^{T}</math><br></center>
    7 KB (1,177 words) - 10:47, 22 January 2015
  • K-Nearest Neighbors Density Estimation This slecture discusses about the K-Nearest Neighbors(k-NN) approach to estimate the density of a given distribution.
    10 KB (1,743 words) - 10:54, 22 January 2015
  • K Nearest Neighbors K Nearest Neighbors is a classification algorithm based on local density estimation.
    9 KB (1,604 words) - 10:54, 22 January 2015
  • <font size="4">From KNN to Nearest Neighbor Classification </font> .... In this tutorial, we will explain first the concept of KNN, secondly the nearest neighbor approach, and thirdly discuss briefly the comparative advantages a
    6 KB (1,013 words) - 10:55, 22 January 2015
  • K-Nearest Neighbors Density Estimation
    931 B (124 words) - 10:55, 22 January 2015
  • *Density Estimation with K-Nearest Neighbors (KNN) **[[K-Nearest Neighbors Density Estimation|Video slecture in English]] by Qi Wang
    8 KB (1,123 words) - 10:38, 22 January 2015

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Alumni Liaison

Questions/answers with a recent ECE grad

Ryne Rayburn