K Nearest Neighbors

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 samplestr_memberships: membership values of traning sampleste: testing samplesk: k value vector (more...

this file you can find a simple but very effective algorithm for Nearest Neighbour Search which I megalomaniacly called the GLTree.You want more? go to the Professional version of GLTreeIt has been designed for uniformly random data, where is the fastest I ever used, but works fine even on sparse...

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...


Platforms: Matlab

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 vectorization. After discussions with John D'Errico, I realized...


Platforms: Matlab

The K-nearest neighbors (K-NN) is an analogous approach. This method has its origin as a non-parametric statistical pattern recognition procedure to distinguish between different patterns according to a selection criterion. Through this method, researchers can generate future data. In other...


Platforms: Windows, Windows 8, Windows 7, Windows Server

In this file you can find a simple but very effective algorithm for Nearest Neighbour Search which I megalomaniacly called the GLTree.You want more? go to the Professional version of GLTreeIt has been designed for uniformly random data, where is the fastest I ever used, but works fine even on...


Platforms: Matlab

When the dataset is small, when you have to run only a few number of search, or when the dimensions of points is large, the brute search method is still faster than kd-trees data structure. Computing the distances one by one take a minor time than building the tree.Some of this problems have...


Platforms: Matlab

IntelligenceLab is a set of .NET 2.0-4.0 components for Artificial Intelligence. Includes: Neural networks, Naive Bayesian, Radial Basis Function Network, Self Organizing Map, K-Nearest Neighbor and more. Usage: Speech recognition, Face recognition, Computer vision, Gesture recognition, Robotics,...


Platforms: Windows

IntelligenceLab 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, Face recognition, Computer vision, Gesture recognition, Robotics,...


Platforms: Windows

Delphi/C++ Builder VCL and FireMonkey (FMX) components library for Artificial Intelligence. Includes: Neural networks, Naive Bayesian, Radial Basis Function Network, Self Organizing Map, K-Nearest Neighbor and more. Usage: Speech recognition, Face recognition, Computer vision, Gesture...


Platforms: Windows

Measures of Analysis of Time Series (MATS) toolkit computes a number of different measures of analysis of scalar time series (linear, nonlinear and other statistical measures). It also contains pre-processing tools (transformations and standardizations), data splitting facility, resampled data...


Platforms: Matlab

A geodesic grid is an even grid over the surface of a sphere. The algorithm is optimized for a grid generated by GridSphere and won't work on an arbitrary geodesic grid. All code is compatible with GNU Octave. The GridSphere function is available on the MATLAB file exchange at...


Platforms: Matlab

XLSTAT is a complete analysis and statistics add-in for Excel. It has been developed since 1993. XLSTAT includes more than 240 features in general or field-specific solutions. The use of Excel as an interface makes XLSTAT a user-friendly and highly efficient statistical and multivariate data...


Platforms: Windows, Windows 8, Windows 7, Windows Server

All this files I've done when I took course in image proc, I haven't changed a thing, hope it'll be useful to students and interested in subject. All codes have comments, it's very easy to understand, as well there is my report file, find there all theoretical background and descriptions.Feature...


Platforms: Matlab

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 "fulltest.m"This implementation offers the following...


Platforms: Matlab

I was playing the game Scramble on my iPod touch, and I realized that I wasn't very good. But the algorithmic challenge of writing a program to solve the Scramble (same as Boggle) game was too tasty to pass up. It was indeed quite challenging, but quite satisfying in the end. The program first...


Platforms: Matlab

LifeLab is a Mac application for exploring John Conway's Game of Life and other cellular automata. CAs were first studied in the mid-1950s by Stanislaw Ulam and John von Neumann. The subject became much more widely known in 1970 when Life was described by Martin Gardner in his Scientific American...


Platforms: Mac

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 differences. This transformation function can be in one of...


Platforms: Matlab

SciCraft is an open source data analysis software developed in Python which allows you to access advanced mathematical functions through an easy-to-use graphical interface. Typical methods can be anything from areas like statistics, chemometrics and artificial intelligence and from languages like...


Platforms: Linux

A MATLAB spectral clustering package to handle large data sets (200,000 RCV1 data) on a 4GB memory general machine. We implement various ways of approximating the dense similarity matrix, including nearest neighbors and the Nystrom method.


Platforms: Windows, Mac, BSD