BIOENGINEER

Fuzzy inference improves Parkinson’s voice classification in two-dataset benchmark

A benchmark study combining entropy-guided partitioning, machine learning and fuzzy inference improved voice-based ...
European Medical Journal

Machine Learning Shows Promise for Alzheimer’s Classification

Machine learning for Alzheimer’s disease classification achieved up to 88.1% accuracy using clinical data, but external ...
BIOENGINEER

Lab study finds about 93% cross-validated accuracy for contactless classification of three breathing patterns

A controlled laboratory study of 56 adults achieved mean cross-validated accuracies of about 93% for contactless ...

Overfitting and Cross-Validation: How to Verify Without Trusting 'It Works in Training'

In the previous article, we covered the "yardstick" for evaluation. At the end, I wrote that if the number of validation data ...
ascopubs.org

Cautionary Note on Using Cross-Validation for Molecular Classification

Health-Related Quality-of-Life Results From the Open-Label, Randomized, Phase III ASPIRE Trial Evaluating Carfilzomib, Lenalidomide, and Dexamethasone Versus Lenalidomide and Dexamethasone in Patients ...
Visual Studio Magazine

Understanding and Using K-Fold Cross-Validation for Neural Networks

Cross-validation is a process that can be used to estimate the quality of a neural network. When applied to several neural networks with different free parameter values (such as the number of hidden ...
Visual Studio Magazine

Understanding and Using K-Fold Cross-Validation for Neural Networks

James McCaffrey walks you through whys and hows of using k-fold cross-validation to gauge the quality of your neural network values. Cross-validation is a process that can be used to estimate the ...