Margin-based loss function广泛应用于各种机器学习和深度学习任务中,包括图像分类、目标检测、语义分割等。 在图像分类任务中,常用的margin-based loss function有softmax loss和hinge loss。softmax loss基于softmax函数,将模型输出转化为类别的概率分布,并通过最小化交叉熵损失来提高模型的分类准确性。hinge loss则通过...
一般来说,Margin-based loss function主要包括两个方面的目标。首先,它试图将所有的样本点都正确地划分到超平面的两侧,即确保所有训练样本点都满足模型对于正负样本的判别要求。其次,它追求最大化超平面与最邻近的正负样本点之间的间距,以提高模型的泛化能力。 常用的Margin-based loss function有Hinge Loss和Squared Hin...
part of the computational graph pos = signed[:n] neg = signed[n:] # negative samples are multiplied by -1, so that the sign in the rankSVM objective is flipped hinge_loss = K.relu( margin - pos - neg ) loss_vec = K.concatenate([hinge_loss, hinge_loss], axis=0) return loss_...
为了解决文档级关系抽取中的长尾多标签问题,本文提出了一种基于Hinge Loss的自适应边界损失函数。它的思想是为每一对实体之间的正类和负类学习一个分隔类。当一个样本被错误地分类或者分类在分隔类的边界附近时,就会触发自适应边界损失函数。通过这个损失函数的优化,可以通过分隔类来增加正类和负类之间的间隔。 在实...
geometric margin, and L(·) is a margin loss defined by functional margins z i = y i f(x i ); i = 1, ··· , n l . Different learning methodologies are defined by different margin losses. Margin losses include, among others, the hinge loss L(z) = (1 − z) +...
This paper incorporated separation (misclassification) measures conforming to conventional discriminative training criterion in loss function definition of margin based method to derive the mathematical framework for acoustic model parameter estimation and discuss some important issues related to hinge loss ...
As known, the supervised feature extraction aims to search a discriminative low dimensional space where the new samples in the sample class cluster tightly and the samples in the different classes keep away from each other. For most of algorithms, how to push these samples located in class marg...
Almost all the available algorithms deal with the imbalanced problems by directly weighting the loss functions.In this paper,a loss by weighting the margin in hinge function is proposed and its Bayesian consistency is proved.Furthermore,a learning algorithm,called Weighting Margin SVM(WMSVM),is obta...
a kernel slack variable is introduced into each base kernel to solve the objective function.Two kinds of soft margin MKL methods based on hinge loss function and square hinge loss function can be obtained when hinge loss functions and square hinge loss functions are selected.The improved methods ...
Then, to address the challenge of adaptive anomaly detection thresholds, this research proposes a nonlinear model of support vector data description (SVDD) utilizing a 0/1 soft-margin loss, referred to as L0/1-SVDD. This model replaces the traditional hinge loss function in SVDD with a 0/1...