现在，为了使其更加直观，让我们检查显示第 10、第 50 和第 90 分位数的分位数损失的图。我们可以使用以下代码片段生成这样的图：import numpy as np import matplotlib.pyplot as plt # 定义 pinball 损失函数def pinball_loss ( y_true, y_pred, quantile ): return np.where(y_true >= y_pred, ...
pinball loss 输出是一个非负浮点数。最佳值为 0.0。 例子: >>> from sklearn.metrics import mean_pinball_loss >>> y_true = [1, 2, 3] >>> mean_pinball_loss(y_true, [0, 2, 3], alpha=0.1) 0.03... >>> mean_pinball_loss(y_true, [1, 2, 4], alpha=0.1) 0.3... >>> mean...
Pinball lossOne-class classificationOutlier detectionMACHINESupport vector data description (SVDD) has been widely used in outlier detection. The conventional SVDD employs the hinge loss function and the sphere classifier is decided by only a small amount of data around the sphere surface (namely ...
Motivated by this observation, we consider the pinball loss, which provides a bridge between the hinge loss and the linear loss. Using this bridge, two 1bit-CS models and two corresponding algorithms are proposed. Pinball loss iterative hard thresholding improves the performance of the binary ...
Traditionally, the hinge loss is used to construct support vector machine (SVM) classifiers. The hinge loss is related to the shortest distance between sets and the corresponding classifier is hence sensitive to noise and unstable for re-sampling. In contrast, the pinball loss is related to the...
HUANG ET AL.: SUPPORT VECTOR MACHINE CLASSIFIER WITH PINBALL LOSS 985 Fig. 1. Data following the p.d.f. shown in (a) are illustrated in (b) and (c), where x i , i ∈ I are marked by green stars and x i , i ∈ II are marked by red crosses. The extreme position in each ...
The original support vector machine (SVM) uses the hinge loss function, which is non-differentiable and makes the problem difficult to solve in particular for regularized SVM, such as with ℓ1-regularized. On the other hand, the hinge loss is sensitive to noise. To circumvent these drawbacks...
a sparse elastic net multi-label RankSVM with pinball loss (pin-ENR) is first proposed in this paper. On the one hand, pinball loss is employed to enhance the robustness. On the other hand, it adopts the sparse elastic net regularization, so that it can do variable selection. However, ...
To guarantee stable quantile estimations even for noisy data, a novel loss function and novel quantile estimators are developed, by introducing the effective concept of orthogonal loss considering the noise in both response and explanatory variables. In particular, the pinball loss used in classical qu...
To improve the generalization performance, we propose a maximum margin and minimum volume hyper-spheres machine with pinball loss (Pin-M3HM) for the imbalanced data classification in this paper. The basic idea is to construct two hyper-spheres with different centers and radiuses in a sequential ...