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Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 6 |
1 files changed, 3 insertions, 3 deletions
@@ -37,11 +37,11 @@ POT provides the following generic OT solvers (links to examples): POT provides the following Machine Learning related solvers: * [Optimal transport for domain - adaptation](https://pythonot.github.io/auto_examples/plot_otda_classes.html) + adaptation](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_classes.html) with [group lasso regularization](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_classes.html), [Laplacian regularization](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_laplacian.html) [5] [30] and [semi supervised setting](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_semi_supervised.html). -* [Linear OT mapping](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_linear_mapping.html) [14] and [Joint OT mapping estimation](https://pythonot.github.io/auto_examples/plot_otda_mapping.html) [8]. -* [Wasserstein Discriminant Analysis](https://pythonot.github.io/auto_examples/domain-adaptation/plot_WDA.html) [11] (requires autograd + pymanopt). +* [Linear OT mapping](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_linear_mapping.html) [14] and [Joint OT mapping estimation](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_mapping.html) [8]. +* [Wasserstein Discriminant Analysis](https://pythonot.github.io/auto_examples/others/plot_WDA.html) [11] (requires autograd + pymanopt). * [JCPOT algorithm for multi-source domain adaptation with target shift](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_jcpot.html) [27]. Some other examples are available in the [documentation](https://pythonot.github.io/auto_examples/index.html). |