---首先根据learning curve来判断你的问题是high bias or variance 当你的算法是high bias问题时,如果你get more training examples是没有用处的,这时我们就不要浪费时间在get5 more training examples上面了。 对如何选择neural network architecture(选择几层hidden layer以及神经网络的大小)的建议 我们可以选择相对于...
To lower chances of overfitting, we restricted learning to a linear SVM; in addition, we enforced directionality of features. This means that we decided upfront whether high values or low values of a feature should improve our confidence in an annotation. For example, a high CSI:FingerID ...
To handle principal component analysis (PCA)-based missing data with high correlation, we propose a novel imputation algorithm to impute missing values, called iterated score regression. The procedure is first to draw into a transformation matrix, which
Conversely, when the prod- uct is negative, it means that the low-level capsule is more likely to be excluded by the high-level capsule. Through this weight update method, the relationship between low-level and high- level features is established, allowing the model to understand the image ...
identifying a practical solution for such an instability, and connecting that solution to the all-important concept of statistical shrinkage. I present a strong link between the following three concepts: regularization of the covariance matrix, ridge regression, andmeasurement error bias, with some easy...
For the coupling model of the wheel–rail interaction, only the vertical and lateral contact forces of the contact points are considered, and the wheelset displacements are related to the contact point displacements of the two rails and the track irregularities by means of a constraint equation. ...
We evaluated the effectiveness of the MHNet framework by measuring five metrics: accuracy (ACC), sensitivity (SEN), specificity (SPEC), the area under the curve (AUC), and the average score (AVG) for each metric accordingly. In order to mitigate bias from a singular dataset split, we empl...
We avoided automated hyperparameter search to limit unintentional method-level overfitting and problems due to adaptive data analysis. We adopted large parts of the U-Sleep model architecture (Supplemen- tary Table 2) and hyperparameters (Supplementary Table 3) from its predecessor U-Time43, which...
[54]. When the theoretical reference population is not representative of the target population for which the model provides a result, there is a risk of bias, error, and overfitting, which can exacerbate health inequalities. For example, “an algorithm designed to predict outcomes from genetic ...
This was essential for avoiding bias in the model’s predictive performance. Specifically, the chosen architecture for the DCNN models was InceptionV3, and the details of its configuration and implementation can be found in the methods section. For the training of DCNN, we first took the patches...