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 K Nearest Neighbor Java 

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This is just a brute force implementation of k nearest neighbor search without using any fancy data structure, such as kd-tree. However it is the fastest knn matlab implementation I can find.

A partial sort mex function is implemented which is a simple wrapper of c++ partial_sort.

Provided the sort function, the matlab code is only of two lines. However, it is extremely fast.

install:
build;



This is a small but efficient tool to perform K-nearest neighbor search, which has wide Science and Engineering applications, such as pattern recognition, data mining and signal processing.

The code was initially implemented through...



A GPU-based efficient data parallel formulation of the k-Nearest Neighbor (kNN) search problem which is a popular method for classifying objects in several fields of research, such as- pattern recognition, machine learning, bioinformatics etc.



A GPU-based efficient data parallel formulation of the k-Nearest Neighbor (kNN) search problem which is a popular method for classifying objects in several fields of research, such as- pattern recognition, machine learning, bioinformatics etc.



A fast artificial intelligence library which currently supports:
kNN (k-Nearest Neighbor algorithm)
MLP (Multilayer-Perceptron)



gaKnn(Genetic Algorithm Optimized K Nearest Neighbor Classification framework) is a frameowork for KNN optimization with a genetic algorithm. The genetic algothm used for this is JGAP (http://jgap.sourceforge.net/).



Delphi/C++ Builder VCL and FireMonkey (FMX) components library for very fast Artificial Intelligence.

Some of the components now also include a GP GPU support.

Also includes Visual Graphical Editor for Codeless Development...



ntelligenceLab is a set of .NET 2.0-4.5 components for Artificial Intelligence.

Also includes a visual graphical editor for codeless development.

Contains:

- Neural Network - Feed forward Neural Network...



IntelligenceLab VC++ is a set of Visual C++ components for Artificial Intelligence.
Includes: Neural networks, Naive Bayesian, Radial Basis Function Network, Self Organizing Map, K-Nearest Neighbor and more.
Usage: Speech recognition,...



This method produces different results depending on what city is choosen as the starting point.

This function determines the Nearest Neighbor routes for multiple starting points and returns the best of those routes.



This implements a KDTree for nearest neighbor and range searching.The KDTree stores a N-dimensional set of points. The tree can be queried for all points within a Euclidian range in order O(sqrt(p)+k) time, where p is the number of points and k is...



Connects randomly ordered 2D points into a minimal nearest neighbor contour.
points2contour
Tristan Ursell
February 2012

[Xout,Yout]=points2contour(Xin,Yin,P,direction)

Given any list of 2D points (Xin,Yin),...



Using Nearest Neighbor-, Linear-, or Bicubic- Interpolation



Fuzzy k-nearest neighbors classifier that can work with training samples whose label info is fuzzified. The prototype is as follows.

[y,predict_class] = f_knn(tr,tr_memberships,te,k)

tr: training samples
tr_memberships:...



Sar-K is a Java tool to generate code for user interfaces, model classes and data access layer based on a database model. Sar-K can access many database engines and produces code in many languages and architectures using customizable templates.



This implementation includes modules for radiometric enhancement of colored 3D point clouds and nearest neighbor search. The introduced method detects the overlapping parts and derives a transformation function that reduces radiometric...



The kd-tree can be used to organize efficient search for nearest neighbors in a k-dimensional space.



kdtree provides a minimalistic implementation of kd-tree. The implementation can be used either inside MATLAB by means of MEX calls, or as a standalone tool, directly from a C/C++ program. The image on the website has been creaed with...



This is an implementation of [1]. The result is slightly different from website[1], because we apply adjacent neighborhood rather than K nearest neighborhood.

The following is an example usage:

%complie:(only once)



function nimg = imresample(oldpixsize,img,newpixsize,intmethod)

% This function resamples the images at the new grid points
% defined by the new pixel sizes. It assumes that intensities are
% defined at pixel centers
%