Accuracy Convergent Field Predictors
This work addresses the need for versatile predictive methods in data analysis, but it appears incremental as it builds on existing field-based approaches.
The paper tackles the problem of developing predictive algorithms that work with categorical, continuous, and mixed data by using field superposition from training instances, and it discusses achieving predictive accuracy convergence as an evaluation criterion.
Several predictive algorithms are described. Highlighted are variants that make predictions by superposing fields associated to the training data instances. They operate seamlessly with categorical, continuous, and mixed data. Predictive accuracy convergence is also discussed as a criteria for evaluating predictive algorithms. Methods are described on how to adapt algorithms in order to make them achieve predictive accuracy convergence.