Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2021
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2112.00365 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914662944079872 |
|---|---|
| author | Papamarkou, Theodore Lindo, Alexey |
| author_facet | Papamarkou, Theodore Lindo, Alexey |
| contents | Probability-generating function (PGF) kernels are introduced, which constitute a class of kernels supported on the unit hypersphere, for the purposes of spherical data analysis. PGF kernels generalize RBF kernels in the context of spherical data. The properties of PGF kernels are studied. A semi-parametric learning algorithm is introduced to enable the use of PGF kernels with spherical data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2112_00365 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Probability-Generating Function Kernels for Spherical Data Papamarkou, Theodore Lindo, Alexey Machine Learning Probability-generating function (PGF) kernels are introduced, which constitute a class of kernels supported on the unit hypersphere, for the purposes of spherical data analysis. PGF kernels generalize RBF kernels in the context of spherical data. The properties of PGF kernels are studied. A semi-parametric learning algorithm is introduced to enable the use of PGF kernels with spherical data. |
| title | Probability-Generating Function Kernels for Spherical Data |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2112.00365 |