Source code for 'Lemna: Explaining deep learning based security applications'.
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May 15, 2020 - Python
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local-regression
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resemble is an R package for similarity-based modelling and local learning in spectroscopy. It provides tools for dissimilarity computation, nearest-neighbour search, memory-based learning, and spectral library optimisation (methods designed for large, heterogeneous spectral datasets where global models underperform)
machine-learning cheminformatics evolutionary-algorithm spectroscopy spectral-data chemometrics nir local-regression pedometrics soil-spectroscopy lazy-learning spectral-library infrared-spectroscopy memory-based-learning evolutionary-search
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Apr 21, 2026 - R
Wind Power Prediction with ECMWF Data as Input using Statistical Learning
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Apr 7, 2020 - PostScript
Comparative analysis of nonparametric regression methods (KNN, LOWESS, Bin, Kernel, Local Linear) to explore the nonlinear relationship between health expenditure and life expectancy using WHO data (2000–2015).
python scikit-learn cross-validation data-visualization global-health statsmodels nonparametric-regression local-regression knn-regression life-expectancy lowess smoothing-methods diminishing-returns who-dataset
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Nov 8, 2025 - Jupyter Notebook
Implementation of LOWESS (Locally Weighted Scatterplot Smoothing) algorithm with bootstrap confidence intervals for nonparametric regression and data smoothing in Python.
education statistics time-series data-visualization forecasting smoothing confidence-intervals outlier-detection statsmodels nonparametric-regression local-regression bootstrap-resampling lowess data-smoothing statmodels scatter-plot-smoothing robust-weighting
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Apr 2, 2026 - Jupyter Notebook
A library of smoothing kernels in multiple languages for use in kernel regression and kernel density estimation.
statistics kernel kernel-methods kernel-smoother nonparametric kernel-density-estimation local-regression kernel-regression
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Feb 9, 2017
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