372 citations · 448 across the 11 of their papers we have counts for
6 papers · 1 filter
LAF-Based Evaluation and UTTL-Based Learning Strategies with MIATTs
Yongquan Yang
In many real-world machine learning (ML) applications, the true target cannot be precisely defined due to ambiguity or subjectivity information. To address this challenge, under th…
Negative Ontology of True Target for Machine Learning: Towards Recognition, Evaluation and Learning under Democratic Supervision
Yongquan Yang
This article philosophically examines how a shift in the assumed ontology of the true target (TT) can lead to a new paradigm for machine learning (ML)-based predictive modelling. B…
One-Step Abductive Multi-Target Learning with Diverse Noisy Label Samples
Yongquan Yang
One-step abductive multi-target learning (OSAMTL) was proposed to handle complex noisy labels. In this paper, giving definition of diverse noisy label samples (DNLS), we propose on…
One-Step Abductive Multi-Target Learning with Diverse Noisy Samples and Its Application to Tumour Segmentation for Breast Cancer
Yongquan Yang, Fengling Li, Yani Wei +4
Recent studies have demonstrated the effectiveness of the combination of machine learning and logical reasoning, including data-driven logical reasoning, knowledge driven machine l…
A Survey on Ensemble Learning under the Era of Deep Learning
Yongquan Yang, Haijun Lv, Ning Chen
Due to the dominant position of deep learning (mostly deep neural networks) in various artificial intelligence applications, recently, ensemble learning based on deep neural networ…
Handling Noisy Labels via One-Step Abductive Multi-Target Learning and Its Application to Helicobacter Pylori Segmentation
Yongquan Yang, Yiming Yang, Jie Chen +2
Learning from noisy labels is an important concern in plenty of real-world scenarios. Various approaches for this concern first make corrections corresponding to potentially noisy-…