*[Decision boundary of label propagation versus SVM on the Iris dataset](https://scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_versus_svm_iris.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-versus-svm-iris-py)
*[Label Propagation learning a complex structure](https://scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_structure.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-structure-py)
*[Label Propagation digits active learning](https://scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py)
参考
[1] Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux. In Semi-Supervised Learning (2006), pp. 193-216
>示例
>
>* [Decision boundary of label propagation versus SVM on the Iris dataset](https://scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_versus_svm_iris.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-versus-svm-iris-py)
>* [Label Propagation learning a complex structure](https://scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_structure.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-structure-py)
>* [Label Propagation digits active learning](https://scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py)
[2] Olivier Delalleau, Yoshua Bengio, Nicolas Le Roux. Efficient Non-Parametric Function Induction in Semi-Supervised Learning. AISTAT 2005 [http://research.microsoft.com/en-us/people/nicolasl/efficient_ssl.pdf](http://research.microsoft.com/en-us/people/nicolasl/efficient_ssl.pdf)
\ No newline at end of file
>参考
>* [1] Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux. In Semi-Supervised Learning (2006), pp. 193-216
>* [2] Olivier Delalleau, Yoshua Bengio, Nicolas Le Roux. Efficient Non-Parametric Function Induction in Semi-Supervised Learning. AISTAT 2005 [http://research.microsoft.com/en-us/people/nicolasl/efficient_ssl.pdf](http://research.microsoft.com/en-us/people/nicolasl/efficient_ssl.pdf)
*[Compare Stochastic learning strategies for MLPClassifier](https://scikit-learn.org/stable/auto_examples/neural_networks/plot_mlp_training_curves.html#sphx-glr-auto-examples-neural-networks-plot-mlp-training-curves-py)
*[Visualization of MLP weights on MNIST](https://scikit-learn.org/stable/auto_examples/neural_networks/plot_mnist_filters.html#sphx-glr-auto-examples-neural-networks-plot-mnist-filters-py)
>示例:
>* [Compare Stochastic learning strategies for MLPClassifier](https://scikit-learn.org/stable/auto_examples/neural_networks/plot_mlp_training_curves.html#sphx-glr-auto-examples-neural-networks-plot-mlp-training-curves-py)
>* [Visualization of MLP weights on MNIST](https://scikit-learn.org/stable/auto_examples/neural_networks/plot_mnist_filters.html#sphx-glr-auto-examples-neural-networks-plot-mnist-filters-py)
## 1.17.3\. 回归
...
...
@@ -132,22 +133,20 @@ array([[0, 1]])
详细信息,请参阅下面的示例。
示例:
*[Varying regularization in Multi-layer Perceptron](https://scikit-learn.org/stable/auto_examples/neural_networks/plot_mlp_alpha.html#sphx-glr-auto-examples-neural-networks-plot-mlp-alpha-py)
>示例:
>* [Varying regularization in Multi-layer Perceptron](https://scikit-learn.org/stable/auto_examples/neural_networks/plot_mlp_alpha.html#sphx-glr-auto-examples-neural-networks-plot-mlp-alpha-py)
*[“Learning representations by back-propagating errors.”](http://www.iro.umontreal.ca/~pift6266/A06/refs/backprop_old.pdf) Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams.
*[“Stochastic Gradient Descent”](http://leon.bottou.org/projects/sgd) L. Bottou - Website, 2010.
*[“Backpropagation”](http://ufldl.stanford.edu/wiki/index.php/Backpropagation_Algorithm) Andrew Ng, Jiquan Ngiam, Chuan Yu Foo, Yifan Mai, Caroline Suen - Website, 2011.
*[“Efficient BackProp”](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf) Y. LeCun, L. Bottou, G. Orr, K. Müller - In Neural Networks: Tricks of the Trade 1998.
*[“Adam: A method for stochastic optimization.”](http://arxiv.org/pdf/1412.6980v8.pdf) Kingma, Diederik, and Jimmy Ba. arXiv preprint arXiv:1412.6980 (2014).
\ No newline at end of file
>参考文献:
>
>* [“Learning representations by back-propagating errors.”](http://www.iro.umontreal.ca/~pift6266/A06/refs/backprop_old.pdf) Rumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams.
>* [“Stochastic Gradient Descent”](http://leon.bottou.org/projects/sgd) L. Bottou - Website, 2010.
>* [“Backpropagation”](http://ufldl.stanford.edu/wiki/index.php/Backpropagation_Algorithm) Andrew Ng, Jiquan Ngiam, Chuan Yu Foo, Yifan Mai, Caroline Suen - Website, 2011.
>* [“Efficient BackProp”](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf) Y. LeCun, L. Bottou, G. Orr, K. Müller - In Neural Networks: Tricks of the Trade 1998.
>* [“Adam: A method for stochastic optimization.”](http://arxiv.org/pdf/1412.6980v8.pdf) Kingma, Diederik, and Jimmy Ba. arXiv preprint arXiv:1412.6980 (2014).