Python package for combining diarization system outputs.
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Updated
Oct 12, 2023 - Python
Python package for combining diarization system outputs.
Python Package for Empirical Statistical Downscaling. pyESD is under active development and all colaborators are welcomed. The purpose of the package is to downscale any climate variables e.g. precipitation and temperature using predictors from reanalysis datasets (eg. ERA5) to point scale. pyESD adopts many ML and AL as the transfer function.
This repository contains an example of each of the Ensemble Learning methods: Stacking, Blending, and Voting. The examples for Stacking and Blending were made from scratch, the example for Voting was using the scikit-learn utility.
Cyber-attack classification in the network traffic database using NSL-KDD dataset
An improved method for predicting toxicity of the peptides and designing of non-toxic peptides
Repo for the OBBStacking: An Ensemble Method for Remote Sensing Object Detection
Pusion (Python Universal Fusion) is a generic and flexible framework written in Python for combining multiple classifier’s decision outcomes.
Splicing detection | ML
This repository contains an implementation for the Dynamic Weighted Ensemble (DWE) - Local Fusion method. Local Fusion is an ensemble techinque that could be used to improve predictions by weighing appropriately the single models contribution.
Genetic Algorithm based Selective Neural Network Ensemble
A project to forecast Stock market prices most efficiently
Skeleton for DVC pipeline to evaluate multiple models together
Performance evaluation of sentiment classification on movie reviews
🎯 Top 204 solution for Elucidata AI Challenge 2025 – Predicting spatial cell-type composition from histology images using CNNs with EfficientNet & ResNet backbones, multi-scale patching, and coordinate-aware ensemble modeling.
A end-to-end ML project for River Flow prediction in Southampton using data from multiple Gov websites
Football Match Predictor for top leagues
Thesis project with title: "Cognitive decline detection using speech features: A machine learning approach"
Semi-Supervised Learning by Disentangling and Self-Ensembling over Stochastic Latent Space. MICCAI 2019.
Kaggle Competition for the final of Machine Learning, URL: https://www.kaggle.com/competitions/superclass-learning. The model to be adapted is professor's thesis code ~ Adapting to the new competition setting
Analyzing Fater company's diaper market potential and enhancing revenue estimation for Naples stores: A Socio-Demographic, Territorial, and Points of Interest Perspective
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