弹性网：变量选择与正则化的强大工具

深度学习
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"《弹性网正则化与变量选择》是一篇由Hui Zou和Trevor Hastie于2005年发表在《英国皇家统计学会学报B》上的论文，该研究提出了弹性网（Elastic Net）这一新的正则化和变量选择方法。论文背景是在现实世界的数据分析中，当预测变量的数量p远大于观测样本数量n时，传统的变量选择方法如Lasso面临挑战。Lasso因其稀疏性（sparsity）而著名，但在这种情况下可能无法有效处理大量关联性强的预测变量。 弹性网结合了Lasso的稀疏性与岭回归（Ridge Regression）的连续性，通过引入两种惩罚项——L1和L2范数，实现了对变量选择的改进。L1惩罚项（Lasso）倾向于产生完全不相关的模型，即“零”或“非零”的特征，而L2惩罚项（Ridge）则对所有特征施加轻度惩罚，防止过拟合。弹性网通过调整两个惩罚的比例，能够在保持模型解释能力的同时，更好地处理高度相关的变量，使得这类变量要么同时被包含在模型中，要么一起被排除，这种特性被称为群组效应（group effect）。 论文通过实证研究和模拟分析表明，弹性网在许多情况下表现出色，尤其是在变量选择和预测性能上，它比Lasso更为稳定。此外，作者提出了一种名为LARS-EN的算法，效仿LARS算法计算弹性网的正则化路径，这使得弹性网的求解过程更为高效，尤其适合大规模数据集。 关键词包括：群组效应、LARS算法、Lasso、惩罚化问题、变量选择。弹性网提供了一个更全面的工具箱，特别是在高维数据和复杂关联性存在的场景中，有助于提高模型的稳健性和准确性。因此，对于数据科学家和机器学习工程师来说，理解并应用弹性网是提升模型性能的关键一步。"

Kernelized Elastic Net Regularization

2015-03-16 上传
标题：“Kernelized Elastic Net Regularization” (内核化的弹性网正则化) 描述：“统计学习框架，本文研究了基于内核的稀疏非线性正则化模型施加的弹性的净正规化。在我们的研究中，实施弹性净正规化的目的是追求...

1.LASSO.pdf

2019-12-03 上传
are stepwise selection procedures, which can be computationally expensive and ignore stochastic errors in the variable selection process. In this article, penalized likelihood approaches are proposed...

论文研究-A Review of Regularization Based Feature Selection Algorithms.pdf

2019-08-27 上传
在模式识别、机器学习以及其他相关领域中，特征选择扮演着至关重要的角色。它主要用在数据预处理阶段，目的是通过选择具有辨别力和高信息量的特征子集来实现高效的降维。随着数据量的不断增长，高维数据处理已经成为...

'L2Regularization', L2Regularization, ...

2023-05-15 上传
'L2Regularization' 是什么？ L2 正则化（L2 regularization）是一种常用的正则化方法，它被广泛应用于机器学习和深度学习中，用于防止模型过拟合。L2 正则化通过在损失函数中添加一个权重衰减项（weight decay ...

Compute the loss and gradients for a two layer fully connected neural network. Inputs: - X: Input data of shape (N, D). Each X[i] is a training sample. - y: Vector of training labels. y[i] is the label for X[i], and each y[i] is an integer in the range 0 <= y[i] < C. This parameter is optional; if it is not passed then we only return scores, and if it is passed then we instead return the loss and gradients. - reg: Regularization strength. Returns: If y is None, return a matrix scores of shape (N, C) where scores[i, c] is the score for class c on input X[i]. If y is not None, instead return a tuple of: - loss: Loss (data loss and regularization loss) for this batch of training samples. - grads: Dictionary mapping parameter names to gradients of those parameters with respect to the loss function; has the same keys as self.params.

2023-05-24 上传
Finally, we add the regularization term to the loss and compute the gradients with respect to the regularization term as well. Here's the code: ```python def two_layer_fc(X, params, reg=0.0): W1, ...

All the 5 fits failed. It is very likely that your model is misconfigured. You can try to debug the ...

2023-05-22 上传
4. Add regularization: If you're overfitting, add regularization techniques such as dropout or L2 regularization. 5. Use pre-trained models: Consider using pre-trained models that have been trained ...

shape regularization effect

2023-05-13 上传
"Shape regularization effect" refers to the phenomenon where a model that is designed to preferentially learn certain types of patterns or structures in the data tends to produce outputs that are ...

Applying Lasso Regularization method and will try to increase accuracy of model

2023-06-08 上传
Lasso regularization is a method used to reduce overfitting in a linear regression model by adding a penalty term to the loss function. This penalty term is the absolute value of the coefficients ...

nn.BatchNorm2d

2023-04-05 上传
Batch normalization is a technique used in deep learning ... Additionally, batch normalization can act as a form of regularization, reducing overfitting and improving the generalization of the model.

The total number of epochs for training was 200. The learning rate for each architecture was kept constant at 0.0001 with the RMSprop31 (root mean square propagation) algorithm, which was used for gradient descent optimization. Though we experimented with different batch sizes (8/16/24/ 32), a batch size of 16 was used for both training and validat- ing the deep convolutional architecture, because it gave the best result. Leaky ReLU (alpha ¼ 0.01), where negative values are occasionally allowed to propagate, was applied in convolutional layers 1 and 2. This provided nonlinearity on the output of the convolutional layers. As all our architec- tures were shallow, to prevent overfitting, both dropout32 and L2 regularization33 were applied before the classification layer. 解释

2023-05-30 上传
这段话主要描述了一个深度卷积神经网络的训练细节。其中，训练的总迭代次数为200次，使用了RMSprop算法作为梯度下降优化器，并设置了学习率为0.0001。在不同的batch size中，选择了16作为训练和验证的批次大小，因为...
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