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Matlab-Machine is a comprehensive collection of machine learning algorithms implemented in MATLAB. It includes both basic and advanced techniques for classification, regression, clustering, and dimensionality reduction. Designed for educational and research purposes, the repository provides clear implementations that help users understand core ML concepts.
This site contains four packages of Mass and mass-based density estimation.
1. The first package is about the basic mass estimation (including one-dimensional mass estimation and Half-Space Tree based multi-dimensional mass estimation). This packages contains the necessary codes to run on MATLAB.
2. The second package includes source and object files of DEMass-DBSCAN to be used with the WEKA system.
3. The third package DEMassBayes includes the source and object files of a Bayesian classifier using DEMass. ...
Simple .m files, Basic Neural Networks study for Octave (or Matlab)
--> For a more detailed description check the README text under the 'Files' menu option :)
The project consists of a few very simple .m files for a Basic
Neural Networks study under Octave (or Matlab).
The idea is to provide a context for beginners that will allow to
develop neural networks, while at the same time get to see and feel
the behavior of a basic neural networks' functioning.
The code is completely open to be modified and may suit several scenarios.
The code commenting is verbose, and variables and functions do respect
English formatting, so that code may be self explanatory.
...
The purpose of this program is to teach a computer to classify plants via their leaves. You just need to input the image of a leaf(acquired from scanner or camera), then the computer can tell you what kind of plant it is.
Bayesian Surprise Matlab toolkit is a basic toolkit for computing Bayesian surprise values given a large set of input samples. It is also useful as way of exploring surprise theory. For more information see also: http://ilab.usc.edu/
MPT is a toolbox that supplies cross-platform libraries
for real-time perception primitives, including face detection, eye detection,
blink detection, and color tracking.