ascopubs.org

Evaluating Dimensionality Reduction for Patient-Reported Outcome–Based Survival Modeling in Patients With Head and Neck Cancer

This study aims to improve survival modeling in head and neck cancer (HNC) by integrating patient-reported outcomes (PROs) using dimensionality reduction techniques. PROs capture symptom severity ...
Visual Studio Magazine

Principal Component Analysis (PCA) from Scratch Using the Classical Technique with C#

Principal component analysis (PCA) is a classical machine learning technique. The goal of PCA is to transform a dataset into one with fewer columns. This is called dimensionality reduction. The ...
CU Boulder News & Events

Dimension Reduction and Classification Using PCA, Factor Analysis and Discriminant Functions - A Short Overview

Tuesday, October 28: Often researchers are faced with data in very high dimensions (e.g. too many predictors for a regression model), or must come up with a rule to classify data in pre-determined ...
VentureBeat

Understanding dimensionality reduction in machine learning models

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Machine learning algorithms have gained fame for being able to ferret out ...
ascopubs.org

Evaluating Dimensionality Reduction for Patient-Reported Outcome–Based Survival Modeling in Patients With Head and Neck Cancer

Performance of Two-Phase Designs for the Time-to-Event Outcome and a Case Study Assessing the Relapse Risk Associated With B-ALL Subtypes This study aims to improve survival modeling in head and neck ...