$L_0$ Regularization of Field-Aware Factorization Machine through Ising Model
Fuente:
arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911788515196928 |
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| author | Okamoto, Yasuharu |
| author_facet | Okamoto, Yasuharu |
| contents | We examined the use of the Ising model as an $L_0$ regularization method for field-aware factorization machines (FFM). This approach improves generalization performance and has the advantage of simultaneously determining the best feature combinations for each of several groups. We can deepen the interpretation and understanding of the model from the similarities and differences in the features selected in each group. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_01718 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | $L_0$ Regularization of Field-Aware Factorization Machine through Ising Model Okamoto, Yasuharu Machine Learning Disordered Systems and Neural Networks We examined the use of the Ising model as an $L_0$ regularization method for field-aware factorization machines (FFM). This approach improves generalization performance and has the advantage of simultaneously determining the best feature combinations for each of several groups. We can deepen the interpretation and understanding of the model from the similarities and differences in the features selected in each group. |
| title | $L_0$ Regularization of Field-Aware Factorization Machine through Ising Model |
| topic | Machine Learning Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2403.01718 |