GitHub

Supervised Learning: Regression & Classification

Predict appliance energy consumption (Wh) from indoor sensor readings and outdoor weather data. Estimate the number of occupants in a room (0–3 people) from multi-modal IoT sensor streams. Implements ...
IEEE

Semi-Supervised Regression via Hierarchical Confidence Screening and Neighbor-Guided Pseudo-Label Calibration

Abstract: Semi-supervised regression enhances model performance by effectively utilizing unlabeled data during training. Most existing methods select only a small subset of high-confidence unlabeled ...
IEEE

Contrastive Learning for Semi-Supervised Deep Regression With Generalized Ordinal Rankings From Spectral Seriation

Abstract: Contrastive learning methods enforce label distance relationships in feature space to improve representation capability for regression models. However, these methods highly depend on label ...
Frontiers

A hybrid VMD-LSTM-SVR model for landslide prediction

Landslides are one of the most prevalent natural geological disasters, causing significant economic losses, damaging public environments, and posing severe threats to human lives. Landslide ...
Scientific Research Publishing

Xie, Z., Jin, J., Liu, D.S., et al. (2024) Construction of a Predictive Model for the Death Risk of Patients with Septic Shock Based on Supervised Machine Learning Algorithms ...

ABSTRACT: Objective: To develop and validate a machine learning-based risk prediction model for postoperative nausea and vomiting (PONV) following gynecological day hysteroscopy, providing ...
BMJ

Prediction of zero-dose children using supervised machine learning algorithm in Tanzania: evidence from the recent 2022 Tanzania Demographic and Health Survey

Objectives This study aimed to employ machine learning algorithms to predict the factors contributing to zero-dose children in Tanzania, using the most recent nationally representative data. Design ...
Visual Studio Magazine

AdaBoost Regression Using C#

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of the AdaBoost.R2 algorithm for regression problems (where the goal is to predict a single numeric value). The ...
Visual Studio Magazine

Simple k-NN Regression Using C#

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of k-nearest neighbors regression to predict a single numeric value. Compared to other machine learning ...