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92 | 92 | <li class="toctree-l2"><a class="reference internal" href="generated/adapt.parameter_based.RegularTransferLR.html">RegularTransferLR</a></li> |
93 | 93 | <li class="toctree-l2"><a class="reference internal" href="generated/adapt.parameter_based.RegularTransferLC.html">RegularTransferLC</a></li> |
94 | 94 | <li class="toctree-l2"><a class="reference internal" href="generated/adapt.parameter_based.RegularTransferNN.html">RegularTransferNN</a></li> |
| 95 | +<li class="toctree-l2"><a class="reference internal" href="generated/adapt.parameter_based.RegularTransferGP.html">RegularTransferGP</a></li> |
95 | 96 | <li class="toctree-l2"><a class="reference internal" href="generated/adapt.parameter_based.FineTuning.html">FineTuning</a></li> |
96 | 97 | <li class="toctree-l2"><a class="reference internal" href="generated/adapt.parameter_based.TransferTreeClassifier.html">TransferTreeClassifier</a></li> |
97 | 98 | <li class="toctree-l2"><a class="reference internal" href="generated/adapt.parameter_based.TransferForestClassifier.html">TransferForestClassifier</a></li> |
| 99 | +<li class="toctree-l2"><a class="reference internal" href="generated/adapt.parameter_based.TransferTreeSelector.html">TransferTreeSelector</a></li> |
| 100 | +<li class="toctree-l2"><a class="reference internal" href="generated/adapt.parameter_based.TransferForestSelector.html">TransferForestSelector</a></li> |
98 | 101 | </ul> |
99 | 102 | </li> |
100 | 103 | <li class="toctree-l1"><a class="reference internal" href="#adapt-metrics">Metrics</a><ul> |
@@ -447,15 +450,24 @@ <h1>ADAPT<a class="headerlink" href="#adapt" title="Permalink to this headline"> |
447 | 450 | <tr class="row-even"><td><p><a class="reference internal" href="generated/adapt.parameter_based.RegularTransferNN.html#adapt.parameter_based.RegularTransferNN" title="adapt.parameter_based.RegularTransferNN"><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameter_based.RegularTransferNN</span></code></a>([task, ...])</p></td> |
448 | 451 | <td><p>Regular Transfer with Neural Network</p></td> |
449 | 452 | </tr> |
450 | | -<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.parameter_based.FineTuning.html#adapt.parameter_based.FineTuning" title="adapt.parameter_based.FineTuning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameter_based.FineTuning</span></code></a>([encoder, task, ...])</p></td> |
| 453 | +<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.parameter_based.RegularTransferGP.html#adapt.parameter_based.RegularTransferGP" title="adapt.parameter_based.RegularTransferGP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameter_based.RegularTransferGP</span></code></a>([...])</p></td> |
| 454 | +<td><p>Regular Transfer with Gaussian Process</p></td> |
| 455 | +</tr> |
| 456 | +<tr class="row-even"><td><p><a class="reference internal" href="generated/adapt.parameter_based.FineTuning.html#adapt.parameter_based.FineTuning" title="adapt.parameter_based.FineTuning"><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameter_based.FineTuning</span></code></a>([encoder, task, ...])</p></td> |
451 | 457 | <td><p>FineTuning : finetunes pretrained networks on target data.</p></td> |
452 | 458 | </tr> |
453 | | -<tr class="row-even"><td><p><a class="reference internal" href="generated/adapt.parameter_based.TransferTreeClassifier.html#adapt.parameter_based.TransferTreeClassifier" title="adapt.parameter_based.TransferTreeClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameter_based.TransferTreeClassifier</span></code></a>([...])</p></td> |
| 459 | +<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.parameter_based.TransferTreeClassifier.html#adapt.parameter_based.TransferTreeClassifier" title="adapt.parameter_based.TransferTreeClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameter_based.TransferTreeClassifier</span></code></a>([...])</p></td> |
454 | 460 | <td><p>TransferTreeClassifier: Modify a source Decision tree on a target dataset.</p></td> |
455 | 461 | </tr> |
456 | | -<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.parameter_based.TransferForestClassifier.html#adapt.parameter_based.TransferForestClassifier" title="adapt.parameter_based.TransferForestClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameter_based.TransferForestClassifier</span></code></a>([...])</p></td> |
| 462 | +<tr class="row-even"><td><p><a class="reference internal" href="generated/adapt.parameter_based.TransferForestClassifier.html#adapt.parameter_based.TransferForestClassifier" title="adapt.parameter_based.TransferForestClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameter_based.TransferForestClassifier</span></code></a>([...])</p></td> |
457 | 463 | <td><p>TransferForestClassifier: Modify a source Random Forest on a target dataset.</p></td> |
458 | 464 | </tr> |
| 465 | +<tr class="row-odd"><td><p><a class="reference internal" href="generated/adapt.parameter_based.TransferTreeSelector.html#adapt.parameter_based.TransferTreeSelector" title="adapt.parameter_based.TransferTreeSelector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameter_based.TransferTreeSelector</span></code></a>([...])</p></td> |
| 466 | +<td><p>TransferTreeSelector : Run several decision tree transfer algorithms on a target dataset and select the best one.</p></td> |
| 467 | +</tr> |
| 468 | +<tr class="row-even"><td><p><a class="reference internal" href="generated/adapt.parameter_based.TransferForestSelector.html#adapt.parameter_based.TransferForestSelector" title="adapt.parameter_based.TransferForestSelector"><code class="xref py py-obj docutils literal notranslate"><span class="pre">parameter_based.TransferForestSelector</span></code></a>([...])</p></td> |
| 469 | +<td><p>TransferForestSelector : Run several decision tree transfer algorithms on a target dataset and select the best one for each tree of the random forest.</p></td> |
| 470 | +</tr> |
459 | 471 | </tbody> |
460 | 472 | </table> |
461 | 473 | </section> |
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