From Linear to Spline-Based Classification:Developing and Enhancing SMPA for Noisy Non-Linear Datasets
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917956258103296 |
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| author | Srivastava, Vatsal |
| author_facet | Srivastava, Vatsal |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_10545 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Linear to Spline-Based Classification:Developing and Enhancing SMPA for Noisy Non-Linear Datasets Srivastava, Vatsal Machine Learning I.5.2 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. |
| title | From Linear to Spline-Based Classification:Developing and Enhancing SMPA for Noisy Non-Linear Datasets |
| topic | Machine Learning I.5.2 |
| url | https://arxiv.org/abs/2503.10545 |