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  • 1
    Machine Learning Homework

    Machine Learning Homework

    Matlab Coding homework for Machine Learning

    ...The repository does not seem to be heavily maintained as a software project; rather, it functions as a library of solved problems and educational examples. The project is useful if you want working MATLAB examples of classic ML techniques, to study, adapt, or compare with your own implementations.
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  • 2
    Phenalysis

    Phenalysis

    Analyze agronomic plant research plots in aerial orthomosaic images.

    A graphical user interface to import, analyze and export plots from orthomosaic images of agronomic trials. Please cite the following reference in your work if you use Phenalysis: Khan Z and Miklavcic SJ (2019) An Automatic Field Plot Extraction Method From Aerial Orthomosaic Images. Front. Plant Sci. 10:683. doi: https://doi.org/10.3389/fpls.2019.00683 This tool is being developed through the sponsorship of the Australian Research Council's Industrial Transformation Research Hub on Wheat in a Hot and Dry Climate. ...
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  • 3
    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...
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  • 4
    Kernel Adaptive Filtering Toolbox

    Kernel Adaptive Filtering Toolbox

    a Matlab benchmarking toolbox for kernel adaptive filtering

    ...This toolbox includes algorithms, demos, and tools to compare their performance. See the included README file for a list of included algorithms and more details. If you use this toolbox in your research please cite: @inproceedings{vanvaerenbergh2013comparative, author = {Van Vaerenbergh, Steven and Santamar{\'i}a, Ignacio}, booktitle = {2013 IEEE Digital Signal Processing (DSP) Workshop and IEEE Signal Processing Education (SPE)}, title = {A Comparative Study of Kernel Adaptive Filtering Algorithms}, year = {2013}, note = {Software available at \url{https://github.com/steven2358/kafbox/}} }
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  • 5
    GURLS

    GURLS

    Grand Unified Regularized Least Squares

    ...Main functionalities already implemented are: * Automatic parameter selection. * Handle massive datasets. * Great modularity, each method can be used independently. * Wide range of optimization routine. Please contact us if you want to join the developers' team or, otherwirse, feel free to download and use the library in your code and send us feedbacks about existing bugs, possible improvements and further developments.
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