Evaluation of machine learning algorithms on hyperspectral images of forests.
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Updated
Oct 23, 2017 - MATLAB
Evaluation of machine learning algorithms on hyperspectral images of forests.
Genetic Algorithm (GA) for making Ensemble of Predictors
Random Forest with CART/C4.5 tree in MATLAB
The Adaboost method for creating a strong binary classifier from a series of weak classifiers is implemented. Classification results are shown for some synthetic datasets and the MNIST dataset containing images of digits.
MATLAB code for Unsupervised Feature Selection with Multi-Subspace Randomization and Collaboration (SRCFS) (KBS 2019)
MATLAB Code for Robust Ensemble Clustering Using Probability Trajectories (IEEE TKDE 2016)
MATLAB Code for Locally Weighted Ensemble Clustering (IEEE TCYB 2018)
Partition relevance analysis with the reduction step
An ANFIS Model for Stock Price Prediction
Matlab source code of the paper "D. Wu and J.M. Mendel, Patch Learning, IEEE Trans. on Fuzzy Systems, 28(9):1996-2008, 2020"
MATLAB code for Enhanced Ensemble Clustering via Fast Propagation of Cluster-wise Similarities (IEEE TSMC-S 2021)
Advanced Data Assimilation Algorithms and Methods
Hybrid Decision Boundary
Cosine Diversity
Repo for the paper "Open-set Recognition for the Detection of Unknown Classes Based on theCombination of Convolutional Neural Networks and Random Forests"
The construction of a majority-voting ensemble based on the interrelation and amount of information of features
An ensemble bagged trees classification approach for monitoring of the engine conditions and fault diagnosis using Visual Dot Patterns of acoustic and vibration Signals
Multiscale Context-aware Ensemble Deep KELM for Efficient Hyperspectral Image Classification, TGRS, 2020.
KDE-based ensemble divergence estimator
Pepelka is a MATLAB toolbox for data clustering and visualization.
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