Summary of Persistence Kernels For Classification: a Comparative Study, by Cinzia Bandiziol et al.
Persistence kernels for classification: A comparative study
by Cinzia Bandiziol, Stefano De Marchi
First submitted to arxiv on: 9 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Algebraic Topology (math.AT)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comparative study of various persistence kernels applied to classification problems. The authors introduce five different kernels and evaluate their performance on several datasets, providing Python codes for reproducibility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper compares different persistence kernels used in classification tasks. It explains the concepts of homology and persistence diagrams and then applies five different kernels to various datasets. The results are presented and Python code is provided so readers can replicate the findings. |
Keywords
» Artificial intelligence » Classification
