regulariser rule
正则化器设置
tax regulariser
添加正则化器
regularisers apply
使用正则化器
regulariser function
调整正则化器
regularisers used
调优正则化器
regulariser method
已应用正则化器
regulariser effect
正则化器已应用
regularisers help
正则化器已移除
we added a regulariser to the loss function to prevent overfitting.
正则化项有助于在模型训练中防止过拟合。
the regulariser term penalises large weights, encouraging simpler models.
我们调节了正则化项的强度，以提升对未见数据的泛化能力。
choosing the right regulariser strength is crucial for model generalisation.
在损失函数中加入正则化项以稳定学习过程。
in deep learning, l2 regulariser is widely used to constrain network parameters.
二范数正则化项会惩罚较大的权重并降低方差。
a dropout layer can act as a regulariser, reducing reliance on specific neurons.
他们使用稀疏性正则化项来鼓励更紧凑的表示。
the researcher tuned the regulariser parameter using cross‑validation.
我们的正则化项系数过高，导致欠拟合。
our approach combines a sparsity‑inducing regulariser with the main objective.
正则化项作为一种约束，抑制过于复杂的解。
when training data is limited, a regulariser helps to stabilise the learning process.
我们在多个基线模型上比较了不同的正则化项选择。
the regulariser effect is more pronounced when the model capacity is high.
合适的正则化项可以提升对噪声标签的鲁棒性。
we compared several regularisers, including l1, l2, and elastic‑net.
正则化项在早期占主导，随后随着拟合改善而减弱。
the regulariser can be applied either to the input features or to the hidden units.
我们移除了正则化项，模型立刻开始过拟合。
implementing a regulariser in the loss yields smoother decision boundaries.
使用正则化惩罚将参数限制在合理范围内。
regulariser rule
正则化器设置
tax regulariser
添加正则化器
regularisers apply
使用正则化器
regulariser function
调整正则化器
regularisers used
调优正则化器
regulariser method
已应用正则化器
regulariser effect
正则化器已应用
regularisers help
正则化器已移除
we added a regulariser to the loss function to prevent overfitting.
正则化项有助于在模型训练中防止过拟合。
the regulariser term penalises large weights, encouraging simpler models.
我们调节了正则化项的强度，以提升对未见数据的泛化能力。
choosing the right regulariser strength is crucial for model generalisation.
在损失函数中加入正则化项以稳定学习过程。
in deep learning, l2 regulariser is widely used to constrain network parameters.
二范数正则化项会惩罚较大的权重并降低方差。
a dropout layer can act as a regulariser, reducing reliance on specific neurons.
他们使用稀疏性正则化项来鼓励更紧凑的表示。
the researcher tuned the regulariser parameter using cross‑validation.
我们的正则化项系数过高，导致欠拟合。
our approach combines a sparsity‑inducing regulariser with the main objective.
正则化项作为一种约束，抑制过于复杂的解。
when training data is limited, a regulariser helps to stabilise the learning process.
我们在多个基线模型上比较了不同的正则化项选择。
the regulariser effect is more pronounced when the model capacity is high.
合适的正则化项可以提升对噪声标签的鲁棒性。
we compared several regularisers, including l1, l2, and elastic‑net.
正则化项在早期占主导，随后随着拟合改善而减弱。
the regulariser can be applied either to the input features or to the hidden units.
我们移除了正则化项，模型立刻开始过拟合。
implementing a regulariser in the loss yields smoother decision boundaries.
使用正则化惩罚将参数限制在合理范围内。
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