Learning in Neural Networks: Theoretical Foundations
Learning in Neural Networks: Theoretical Foundations
A Simple Approach to Ordinal Classification
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
The Journal of Machine Learning Research
Covering number bounds of certain regularized linear function classes
The Journal of Machine Learning Research
Gaussian Processes for Ordinal Regression
The Journal of Machine Learning Research
Neural Computation
Prediction of Ordinal Classes Using Regression Trees
Fundamenta Informaticae - Intelligent Systems
Support Vector Ordinal Regression
Neural Computation
Learning to Classify Ordinal Data: The Data Replication Method
The Journal of Machine Learning Research
ROC analysis in ordinal regression learning
Pattern Recognition Letters
Stability and generalization of bipartite ranking algorithms
COLT'05 Proceedings of the 18th annual conference on Learning Theory
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The problem of ordinal regression, in which the goal is to learn a rule to predict labels from a discrete but ordered set, has gained considerable attention in machine learning in recent years. We study generalization properties of algorithms for this problem. We start with the most basic algorithms that work by learning a real-valued function in a regression framework and then rounding off a predicted real value to the closest discrete label; our most basic bounds for such algorithms are derived by relating the ordinal regression error of the resulting prediction rule to the regression error of the learned real-valued function. We end with a margin-based bound for the state-of-the-art ordinal regression algorithm of Chu & Keerthi (2007).