Large-scale nonlinear AUC maximization via triply stochastic gradients
Learning to improve AUC performance for imbalanced data is an important machine
learning research problem. Most methods of AUC maximization assume that the model …
learning research problem. Most methods of AUC maximization assume that the model …
MBA: mini-batch AUC optimization
Area under the receiver operating characteristics curve (AUC) is an important metric for a
wide range of machine-learning problems, and scalable methods for optimizing AUC have …
wide range of machine-learning problems, and scalable methods for optimizing AUC have …
An adaptive mini-batch stochastic gradient method for AUC maximization
Due to the wide applications in imbalanced learning, directly optimizing AUC has gained
increasing interest in recent years. Compared with traditional batch learning methods, which …
increasing interest in recent years. Compared with traditional batch learning methods, which …
Scalable nonlinear auc maximization methods
The area under the ROC curve (AUC) is a widely used measure for evaluating classification
performance on heavily imbalanced data. The kernelized AUC maximization machines have …
performance on heavily imbalanced data. The kernelized AUC maximization machines have …
Towards interpretation of pairwise learning
Recently, there are increasingly more attentions paid to an important family of learning
problems called pairwise learning, in which the associated loss functions depend on pairs of …
problems called pairwise learning, in which the associated loss functions depend on pairs of …
Online Semi-supervised Pairwise Learning
Online learning is a machine learning method that sequentially updates the predictive
model. It is a significant learning technique for massive and streaming data, where it is …
model. It is a significant learning technique for massive and streaming data, where it is …
Enhancing Personalized Ranking With Differentiable Group AUC Optimization
AUC is a common metric for evaluating the performance of a classifier. However, most
classifiers are trained with cross entropy, and it does not optimize the AUC metric directly …
classifiers are trained with cross entropy, and it does not optimize the AUC metric directly …
Proximal stochastic AUC maximization
This work considers a stochastic optimization problem for maximizing the AUC (area under
the ROC curve). The AUC metric has proven to be a reliable performance measure for …
the ROC curve). The AUC metric has proven to be a reliable performance measure for …
WEDA: A Weak Emission-Line Detection Algorithm Based on the Weighted Ranking
Y Zhou, H Yang, J Cai, X Zhao, Y Xun, C Qu - IEEE Access, 2020 - ieeexplore.ieee.org
The Hα emission line in rest wavelength frame of optical spectra is valuable characteristics
for nebulae detection. Searching and recognizing the spectra with Hα emission line from …
for nebulae detection. Searching and recognizing the spectra with Hα emission line from …
[PDF][PDF] Determine and explain confidence in predicting violations on inland ships in the Netherlands
P Bakker - 2020 - repository.tudelft.nl
For real-world problems even the most complex machine learning models can only achieve
a certain accuracy. This makes it important to understand why a specific prediction is made …
a certain accuracy. This makes it important to understand why a specific prediction is made …