Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL, USA. Ordinal outcome neural networks represent an innovative and robust methodology for analyzing high-dimensional ...

This paper argues that currently available methods for the assessment of the repeatability and reproducibility of ordinal classifications are not satisfactory. The paper aims to study whether we can ...

PR1, W1, T51, F58, SL4, KL3, SM11. This is not a test to crack a code. But you will see a series of letter and number combinations while engaging with the Paralympics in Paris. At the Olympics, there ...

Abstract: We develop a deep neural network for ordinal classification and use this technique for the automatic cloud cover assessment (ACCA) of satellite images. We adopt a VGG+ResNet approach with a ...

A team from the University of Cordoba has designed a model, based on fuzzy logic, that predicts the performance of online education students, dividing them into 4 categories and helping professors ...

I am working on a classification problem where the target values have some order to them. I would appreciate a native objective function that increases the penalty for a prediction that is further ...

I think this would be a powerful thing to implement for catboost. Even sklearn does not have an in-house ordinal classifier or estimator wrapper. There's some toy built classifiers and wrappers out ...

Dr. James McCaffrey of Microsoft Research presents a simple technique he has used with good success, previously unpublished and without a standard name. The goal of an ordinal classification problem ...