From Linear to Spline-Based Classification:Developing and Enhancing SMPA for Noisy Non-Linear Datasets
This work addresses classification challenges for noisy non-linear datasets, but it appears incremental as it builds upon existing MPA concepts.
The paper tackles the problem of classification with non-linear decision boundaries by extending the Moving Points Algorithm (MPA) to incorporate cubic splines, and analyzes training results on synthetic datasets with known properties, though no concrete numbers are provided.
Building upon the concepts and mechanisms used for the development in Moving Points Algorithm, we will now explore how non linear decision boundaries can be developed for classification tasks. First we will look at the classification performance of MPA and some minor developments in the original algorithm. We then discuss the concepts behind using cubic splines for classification with a similar learning mechanism and finally analyze training results on synthetic datasets with known properties.