There should be different requirements for False Reject rate and False Accept rate in classification applications, and classifier learning should use an ...

There should be different requirements for False Reject rate and False Accept rate in classification applications, and classifier learning should use an ...

Abstract：. There should be different requirements for False Reject rate and False Accept rate in classification applications, and classifier learning should ...

A novel AdaBoost algorithm was developed with the asymmetric weight. Moreover we provide the theoretical analysis of its performance and derive the upper bound ...

Feb 14, 2018 · In this letter, we introduce an asymmetric adaptation neural network (AANN) method for cross-domain classification in remote sensing images.

This algorithm, known as the aaPU (Adaptive Asymmetric Positive Unlabeled) algorithm, provides near supervised classification accuracy with very low levels of ...

In this paper, we proposed a novel asymmetric tri-training method for unsupervised domain adaptation, which is im- plemented in a simple manner. We aimed at ...

To address the data imbalance problem, we pro- pose an asymmetric adaptation paradigm, namely AsyFOD, which leverages the source and target instances from ...

Feb 27, 2017 · We propose an asymmetric tri-training method for unsupervised domain adaptation, where we assign pseudo-labels to unlabeled samples and train neural networks ...

This paper proposes a new end-to-end diagnostic method based on a recently emerged graph convolutional neural network for the diagnosis of ASD in children.