Project for Digital Image Processing
IRLS(Iterative re-weighted least square) for Logistic Regression, implemented using tensorflow2.0/pytorch
A python implementation of linear classification algorithm (including Probabilistic Generative Model, Probabilistic Discriminative Model). (See Pattern Recognition and Machine Learning, Bishop)
This is the implementation of Iterative Reweighted Least Square Optimization of Logistic Regression
Recommended reading material pertaining to MUPE and other multiplicative error regression techniques
Open Source Notrufabfrage (OpenSNA) — Standardisierte, rechtssichere und modulare Meldebildabfrage für Feuerwehr und Rettungsdienst
A complete mathematical derivation of Generalized Linear Models, along with an exemplification via a from-scratch implementation of logistic regression.
Logistic Regression with different optimizers in Python from scratch
A hands‑on, first‑principles guide to fitting logistic regression via the Iteratively Reweighted Least Squares (IRLS) algorithm complete with mathematical derivations, R code from scratch, and a real‑world S&P data case study to bring your statistical modeling skills to the next level.
An iteratively reweighed sandbox
The Asymmetric Least Squares performs baseline correction by penalizing positive residuals.
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